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Reinforcement Learning for the Control of Autonomous Robots

Reinforcement Learning for the Control of Autonomous Robots

Posted on | by Duckietown Admin

Reinforcement Learning for the Control of Autonomous Robots

Project Resources
  • Objective: Develop and evaluate reinforcement learning (RL) techniques for safe and autonomous navigation in any Duckietown
  • Approach: Develop, train and test RL algorithms including Deep Q-Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO), for autonomous lane-keeping and obstacle detection on a DB21 Duckiebot.
  • Authors: Bruno Fournier, Sébastien Biner
Report
Final Result
Authors

RL on Duckiebots - Project highlights

Here is a visual tour of the authors’ work on implementing reinforcement learning in Duckietown.

Diagram illustrating the basic principle of reinforcement learning applied to autonomous driving, showing an agent interacting with the environment, making decisions based on rewards and feedback.
Figure 1. Principle of Reinforcement Learning in Autonomous Driving.
Diagram showing the application of reinforcement learning in the Duckietown environment, with a Duckiebot navigating simulated roadways based on RL feedback.
Figure 2. Reinforcement Learning in the Duckietown Environment.
Comparison diagram showing the differences between Q-learning and Deep Q-Networks (DQN).
Figure 3. Q-learning vs. Deep Q-Networks (DQN).
Diagram depicting the learning process with the Deep Q-Network (DQN) model, showing how actions are taken based on state inputs and updated using Q-value estimations.
Figure 4. Learning Process with the Deep Q-Network (DQN) Model.
Diagram illustrating the architecture of the Deep Deterministic Policy Gradient (DDPG) algorithm, highlighting the actor and critic networks, experience replay, and target networks.
Figure 5. Architecture of the Deep Deterministic Policy Gradient (DDPG) Algorithm.
Image of a simulation environment in Duckietown, displaying a virtual map with roads, intersections, and a Duckie.
Figure 6. Simulation Environment in Duckietown.
Diagram of the Duckiebot test track with modular square elements, including straight lines, right-angle turns, and intersections, illuminated by two Walimex Pro LED lamps.
Figure 7. Modular Test Track for Duckiebot Driving Tests.
Diagram illustrating the Duckiebot’s reward factors: the distance from the center of the lane (laned) and the angle relative to the lane’s centerline (laneθ), used in the DQN reward function.
Figure 8. Reward Factors for DQN in Duckiebot Navigation.
Side-by-side images showing line detection in Duckietown before and after HSV parameter correction, illustrating improved clarity and accuracy of detected lines.
Figure 9. Line Detection Improvement with HSV Parameter Correction.
Diagram illustrating the structure of the PA2 DQN model, showing the pre-processing of RGB images before they are fed into the neural network for reinforcement learning.
Figure 10. DQN Model Structure.
Aerial view of the "loop_empty" training map used for DQN model training, featuring straight sections and both left and right turns.
Figure 11. Training Map for DQN.
Illustration highlighting the differences between simulation and reality in the context of Duckietown, including variations in color tones, camera angles, and environmental objects.
Figure 12. Differences Between Simulation and Reality.
Graph showing the average reward and average episode length for the DQN model in PA2 over multiple training episodes.
Figure 13. DQN (PA2): Average Reward and Average Episode Length.
Graph showing episode-based rewards during the first phase of DDPG training.
Figure 14. DDPG Training 1: Episode-Based Rewards.
Graph displaying episode-based rewards during the second phase of DDPG training.
Figure 15. DDPG Training 2: Episode-Based Rewards.
Graph showing the average rewards achieved during the training process.
Figure 16. Average Rewards During Training.
Graph showing the average distance traveled during each episode throughout the training.
Figure 17. Average Distance Traveled During Episodes.
Graph showing the evolution of the agent's speed throughout the training process.
Figure 18. Evolution of the Agent's Speed.
Graph showing the average reward and average episode length during Trial 1 of training.
Figure 19. Trial 1: Average Reward and Average Episode Length.
Graph showing the average reward and average episode length during Trial 2 of training.
Figure 20. Trial 2: Average Reward and Average Episode Length.
Graph showing the average reward and average episode length during Trial 3 of training.
Figure 21. Trial 3: Average Reward and Average Episode Length.
Graph showing the average reward and average episode length during Trial 4 of training.
Figure 22. Trial 4: Average Reward and Average Episode Length.
Visualization of the agent's trajectory on the evaluation track during testing.
Figure 23. Agent Trajectory on the Evaluation Track.
Graph showing the average reward and average episode length during Trial 5 of training.
Figure 24. Trial 5: Average Reward and Average Episode Length.
Graph showing the average reward and average episode length during Trial 6 of training.
Figure 25. Trial 6: Average Reward and Average Episode Length.
Visualization of the robot's trajectory as it negotiates a bend on the track.
Figure 26. Trajectory Taken by the Robot to Negotiate a Bend.
Graph showing the evolution of the safety factor throughout the training process.
Figure 27. Evolution of the Safety Factor.

Why reinforcement learning for the control of Duckiebots in Duckietown?

This thesis explores the use of reinforcement learning (RL) techniques to enable autonomous navigation in the Duckietown. Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment and receiving feedback through rewards or penalties. The goal is to maximize long-term rewards.

This work focuses on implementing and comparing various RL algorithms—specifically Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) – to analyze performance in autonomous navigation. RL enables agents to learn behaviors by interacting with their environment and adapting to dynamic conditions. The PPO model was found demonstrating smooth driving using grayscale images for enhanced computational efficiency.

Another feature of this project is the integration of YOLO v5, an object detection model, which allowed the Duckiebot to recognize and stop for obstacles, improving its safety capabilities. This integration of perception and RL enabled the Duckiebot not only to follow lanes but also to navigate autonomously, making ‘real-time’ adjustments based on its surroundings.

By transferring trained models from simulation to physical Duckiebots (Sim2Real), the thesis evaluates the feasibility of applying these models to real-world autonomous driving scenarios. This work showcases how reinforcement learning and object detection can be combined to advance the development of safe, autonomous navigation systems, providing insights that could eventually be adapted for full-scale vehicles.

Learn about Implementing Machine Learning with Duckietown

Reinforcement learning for the control of Duckiebots in Duckietown - the challenges

Implementing reinforcement learning, in this project faced a number of challeneges summarized below – 

  • Transfer from Simulation to Reality (Sim2Real): Models trained in simulations often encountered difficulties when applied to real-world Duckiebots, requiring adjustments for accurate and stable performance.
  • Computational Constraints: Limited processing power on the Duckiebots made it challenging to run complex RL models and object detection algorithms simultaneously.
  • Stability and Safety of Learning Models: Guaranteeing that the Duckiebot’s actions were safe and did not lead to erratic behaviors or collisions required fine-tuning and extensive testing of the RL algorithms.
  • Obstacle Detection and Avoidance: Integrating YOLO v5 for obstacle detection posed challenges in ensuring smooth integration with RL, as both systems needed to work harmoniously for obstacle avoidance.

These challenges were addressed through algorithm optimization, iterative model testing, and adjustments to the hyperparameters.

Reinforcement learning for the control of Duckiebots in Duckietown: Results

Reinforcement learning for the control of Duckiebots in Duckietown: Authors

Bruno Fournier

Bruno Fournier is currently pursuing Master of Science in Engineering, Data Science at the HES-SO Haute école spécialisée de Suisse occidentale, Switzerland.

Sébastien Biner

Sébastien Biner is currently pursuing Bachelor of Science in Automotive and Vehicle Technology at the Berner Fachhochschule BFH, Switzerland.

Learn more

Duckietown is a modular, customizable and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

It is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

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Autonomous Calibration - Wheels & Camera in Duckietown

Autonomous Calibration – Wheels and Camera in Duckietown

Posted on | by Duckietown Admin
General Information
  • Title: Autonomous Wheels And Camera Calibration In Duckietown Project
  • Authors: Kirill Krinkin, Konstantin Chayka, Anton Filatov, Artyom Filatov
  • Institution: Saint Petersburg Electrotechnical University, Russia
  • Citation: Krinkin, K., Chayka, K., Filatov, A. and Filatov, A., 2021. Autonomous wheels and camera calibration in duckietown project. Procedia Computer Science, 186, pp.169-176.
Paper
Institution
Authors

Autonomous Calibration – Wheels and Camera in Duckietown

In robotics, accurate calibration of components like cameras and wheels is essential for precise operation. This research is focused on developing an autonomous calibration system for Duckiebots image sensors and odometry.

Traditional calibration methods require manual intervention, often taking time and relying on human accuracy, which can introduce variability. The paper presents a fully autonomous approach to calibration, enabling Duckiebots to perform self-calibration without human guidance. This enables users to calibrate multiple robots simultaneously, maximizing efficiency and reducing downtime.

Fiducial markers (AprilTags) are utilized in pre-marked environments. Although the method showed slightly reduced calibration precision compared to typical alternatives, the process still yields sufficient performance for Duckiebots to navigate autonomously in Duckietown.

Learn about calibrations with Duckietown materials

Highlights - Autonomous Calibration - Wheels and Camera in Duckietown

Here is a visual tour of the work of the authors. For all the details, check out the full paper.

Initial position of a robot Duckiebot for autonomous calibration.
Figure 1. Initial position of the Duckiebot.
State to state algorithm of calibraion.
Figure 2. State to state algorithm of calibraion.
Reprojection error and straight line deviations
Figure 3. Reprojection error and straight line deviations.

Abstract

In the author’s words:

After assembling the robot, it is necessary to calibrate its components such as camera and wheels for example. This requires human participation and depends on human factors. The article describes the approach to fully automatic calibration of the camera and the wheels of the robot. 

It consists in placing the robot in an inaccurate position, but in a pre-marked area and using data from the camera, information about the configuration of the environment. As well as the ability to move, to perform calibration without the participation of external observers or human participation. There are 2 stages: camera and wheels calibration. 

Camera calibration collects the necessary set of images by automatically moving the robot in front of the fiducial markers template, and moving the robot on the marked floor with an estimation of the curvature of the trajectory. Proposed approach was experimentally tested on the duckietown project base.

Conclusion - Autonomous Calibration - Wheels and Camera in Duckietown

Here are the conclusions from the authors of this paper:

“As a result, a solution was developed that allows fully automatic calibration of the camera and robot wheels in the Duckietown project. The main feature is the autonomy of the process, which allows one person to run in parallel the calibration of an arbitrary number of robots and not be blocked during their calibration. 

The limitation is the number of physically labeled sites. According to the results of comparing the developed solution with the initial one, a slight deterioration in accuracy can be noted, which is primarily associated with the accuracy of the camera calibration, however, the result obtained is nevertheless sufficient for the initial calibration of the robot and is comparable to manual calibration. 

As the planned improvements, which will have to increase the accuracy of the camera calibration, a larger number of chessboards located at different angles and a greater distance of movement used in calibrating the wheels will be used.”

Project Authors

Kirill Krinkin

Kirill Krinkin is an Adjunct Professor at Constructor University, Germany.

Konstantin Chaika

Konstantin Chaika is an Educational Content Manager, Tutor at JetBrains, Czech Republic.

Anton Filatov

Anton Filatov is currently affiliated with the Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia.

Artyom Filatov is currently affiliated with the Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia.

Learn more

Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

It is modular, customizable and state-of-the-art, and designed to teach, learn, and do research. From exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge, Duckietown evolves with the skills of the user.

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Smart Lighting: Realistic Day and Night in Duckietown

Smart Lighting: Realistic Day and Night in Duckietown

Posted on | by Duckietown Admin

Smart Lighting: Realistic Day and Night in Duckietown

Project Resources
  • Objective: City Realism: Characterising Night and Day Autonomous Driving Performance in the Duckietown Autolab
  • Approach: Developing an automated street "smart" lighting system and control models, including a PI controller.
  • Authors: David Müller
Report
Final Result
Authors

Project Highlights

Here is the output of the authors’ work on smart lighting autonomous driving.

A diagram showing the flow of nodes in the image processing pipeline, from camera input to lane detection using color filtering and line detection to enable smart lighting autonomous driving in Duckietown.
Figure 1. Image Processing Pipeline for Duckiebot Lane Detection.
PID controller
Figure 2. PID controller.
A diagram illustrating the open loop control system of the Duckietown street lighting system, showing the interaction between light sources and the image processing pipeline.
Figure 3. Open Loop Control of Duckietown Street Lighting System.
A pair of wooden streetlight prototypes designed by Aurel Neff, positioned in Duckietown to provide lighting for smart lighting autonomous driving experiments.
Figure 4. Aurel Neff's Wooden Light Stands for Duckietown.
Open loop of lighting system
Fig.5 Open loop of lighting system
A control loop diagram showing the Duckiebot as the sole sensor for managing street lighting in Duckietown, using detected segments to control lighting conditions.
Figure 6. Control Loop with Duckiebot as the Only Sensor.
A feedback loop diagram showing how the Duckiebot controls its own smart lighting system using detected lane segments and environmental lighting conditions.
Figure 7. Feedback Control Loop of Duckiebot Lighting System.
A control loop diagram showing the watchtower's camera acting as a sensor to manage street lighting in Duckietown, influencing the Duckiebot's lane detection.
Figure 8. Control Loop with Watchtower Camera as Sensor.
A feedback loop diagram illustrating how the watchtower's camera acts as a sensor to control the street lighting system in Duckietown.
Figure 9. Feedback Loop with Watchtower Camera as Sensor.
A control loop diagram showing how the RGB sensor in the watchtower is used to manage street lighting conditions in Duckietown.
Figure 10. Control Loop Using RGB Sensor as Sensor.
A graphical representation showing the effect of updated color ranges on the color detection capabilities of the Duckiebot in Duckietown.
Figure 13. Impact of Modified Color Ranges on Detection.
Investigated lighting conditions in RGB space. The colored dots illustrates the colors of the edges of the grid.
Figure 14. Investigated lighting conditions in RGB space. The colored dots illustrates the colorsof the edges of the grid
A comparison of (a) lane locations considered valid and (b) segments detected by the Duckiebot in Duckietown.
Figure 15. Filtering of Detected Segments in Duckietown.
An image depicting the experimental setup used to assess optimal lighting conditions for the Duckiebot on a straight street in Duckietown.
Figure 16. Experimental Setup for Evaluating Optimal Lighting Conditions on a Straight Street.
An image depicting the experimental setup used to assess the Duckiebot lighting system, highlighting that the WT04 watchtower is not operational.
Figure 17. Experimental Setup for Evaluating the Duckiebot Lighting System with Non-Functional WT04.
Measured values for d and phi while the Duckiebot is following the lane
Figure 18. Lane following performance of the Duckiebot at different and changing light condition of the ceiling light and the street lighting system controlling the light on the streets of Duckietown

Why day and night autonomous driving in Duckietown?

Autonomous driving is already inherently hard. Driving at night makes it even more challenging! This is why smart lighting is an interesting application that intersects with autonomous driving: having city infrastructure, such as traffic lights and watchtowers, generate dynamically varying light – only where and when they’re needed – to make driving at night not only possible but safe. Here are some reasons for which this project is interesting:

Realistic driving scenarios: autonomous driving systems must handle varying lighting conditions. Day and night cycles are just the beginning: transitions like sunrise or sunset make the spectrum of experimental corner cases more complex, hence Duckietown a valuable testbed.

Robust lane-following capabilities: developing an adaptive lighting system in which the city infrastructure “collaborates” with Duckiebot to provide optimal driving scenarios reinforces driving performances and general robustness for lane following.  

Decentralized control for scalability: a decentralized approach to managing lighting implies that the system can be scalable across Duckietowns of arbitrary dimensions, making it more adaptable and resilient.

Autonomous lighting management: a responsive street lighting system, working in tandem with the Duckiebot’s onboard sensors, improves energy efficiency and ensures safety by adjusting to local lighting needs automatically.

Learn about Advanced Autonomy Approaches with Duckietown

Smart Lighting: Realistic Day and Night in Duckietown - the challenges

Implementing smart lighting in Duckietown to improve autonomous driving during day and night cycles presents several challenges. Here are a few examples: 

Hardware modifications: while Duckiebots are equipped with controllable LEDs, city infrastructure does not possess lighting capabilities out of the box. The first step is integrating light sources in the design of Duckietown’s city infrastructure.

Variable lighting conditions: Duckiebots, which in this project rely uniquely on vision in their autonomy pipeline, must adapt to changing lighting conditions such as full darkness, sunrise, sunset, and artificial lighting, which impacts camera vision and lane detection accuracy.

Decentralized control: managing street lighting in a decentralized way across Duckietown ensures that each area adapts to its local lighting needs, compensating for example for the presence of passing Duckiebots with their own lights on. Join control algorithms including both city infrastructure and vehicle lighting intensity add complexity to the system’s design and coordination.

Scalability: the street lighting system must be scalable across the entire city, requiring a design that can be expanded without significant complications.

Safe and reliable operation: the system needs to be safe, adapting to issues such as occasional watchtower lighting source failure, while ensuring consistent lane-following performance.

Smart Lighting: Realistic Day and Night in Duckietown: Results

Smart Lighting: Realistic Day and Night in Duckietown: Authors

David Müller

David Müller is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently works as a Research Engineer at Disney Research, Switzerland.

Learn more

Duckietown is a modular, customizable and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

It is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

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Multi-camera multi-robot visual localization system

Visual localization using multi-camera multi-robot system

Posted on | by Duckietown Admin
General Information
Paper
Institution
Authors

Visual localization using multi-camera multi-robot system

Visual robot localization is a crucial problem in robotics: how to estimate the agents’ position using vision.

A common approach to solving it is through Simultaneous Localization and Mapping (SLAM) algorithms, using onboard sensors to map and estimate robot positions.

This work introduces a new algorithm for robot localization using AprilTag fiducial markers. It works on a rectangular map with four corner tags, requiring minimal configuration and offering flexibility in camera positions.

Unlike prior methods, this algorithm automatically stitches images from cameras, regardless of angle, and converts them into a top-down view for robot localization.

The approach promises flexibility, making adapting to dynamic camera setups easier without reconfiguration.

This solution offers automated robot localization with minimal setup, leveraging computer vision and AprilTags for more efficient mapping. The only constraint is the rectangular shape of the map and properly oriented corner markers, making it an ideal fit for scalable, adaptive robot environments.

Learn about robot autonomy, including perception, localization, and SLAM, starting from the link below!

Duckietown Educational Resources

Abstract

In the author’s words:

The article presents a general framework for detecting the boundaries of, stitching, adjusting perspective and finally localizing robot positions and azimuth angles for any rectangular map designated with AprilTag markers in the corners and possibly in the interior area. 

At the same time, the focus of the researchers was to minimize the configuration required for the algorithm to operate – here limited to just the orientation and data of markers, dimensions of the map, markers and robots. 

The location of cameras can be freely changed without the need to reconfigure anything or restart the program. This work has been tested on and turned out to be especially helpful for working with the Duckietown project.

 

Highlights - Visual localization using multi-camera multi-robot system

Here is a visual tour of the work of the authors. For more details, check out the full paper.

A miniature Duckietown setup with small robot vehicles, roads, and AprilTag markers used for testing visual localization and autonomous navigation.
Figure 1. Duckietown Test Environment.
A diagram showing common features (corner points) between two images captured by different cameras, used for stitching in the homography matrix algorithm.
Figure 2. Common Features Between Two Cameras.
A diagram illustrating the stitching step masks, where overlapping areas of images are blended with equal weights, and disjoint areas are added with full opacity.
Figure 3. Stitching Step Masks for Image Blending.
A stitched image with magenta points marking the map's corner boundaries, white cross marks for the corner markers' top-left corners, and red/blue crosses for inner markers.
Figure 4. Stitched Image with Map Boundaries and Marker Corners.
A diagram showing corner reprojection to a top-down perspective with colorful lines connecting matching points, orange centroids of detections, and white labels for corner codes.
Figure 5. Corner Reprojection to Top-Down Perspective.
A map showing robot positions marked by green crosses, with azimuth angles relative to north and coordinates represented as percentages of the map's dimensions.
Figure 6. Robot Position and Azimuth in Map Coordinates.
A stitched image showing the extrapolation of one missing corner, where the algorithm estimates its position to create a complete view of the map.
Figure 7. Extrapolation of Missing Corner in Stitched View.
A reprojection view showing the extrapolation of one missing corner, with the algorithm estimating the corner's position for a complete top-down perspective.
Figure 8. Extrapolation of Missing Corner in Reprojection View.
The final result of missing corner extrapolation, showing a complete map with the estimated corner integrated smoothly into the overall environment.
Figure 9. Final Result of Missing Corner Extrapolation.
A view showing common features in an image with no shared corners and three total corners, illustrating feature detection without overlapping corner points.
Figure 10. Common Features View with Zero Common Corners.
A stitched image where no corners are shared but three total corners are present, showing how the algorithm combines images despite the absence of common corners.
Figure 11. Stitched View with Zero Common Corners.
A reprojection view showing a scenario with no shared corners and three total corners, where the algorithm forms a complete top-down perspective despite the lack of common corners.
Figure 12. Reprojection View with Zero Common Corners.
The result view showing a complete image with zero common corners and three total corners, illustrating the successful integration of data without overlapping features.
Figure 13. Result View with Zero Common Corners.
A common features view showing zero common corners and three total corners, highlighting one common feature detected by the algorithm despite the lack of overlapping corners.
Figure 14. Common Features View with One Overlapping Feature.
A stitched image showing zero common corners and three total corners, highlighting one common feature detected, demonstrating the algorithm's ability to combine images effectively.
Figure 15. Stitched View with One Common Feature.
A reprojection view showing zero common corners and three total corners, emphasizing one common feature detected by the algorithm, illustrating effective data integration.
Figure 16. Reprojection View with One Common Feature.
The result view showing zero common corners and three total corners, highlighting one common feature detected by the algorithm, demonstrating successful data integration.
Figure 17. Result View with One Common Feature.

Conclusion - Visual localization using multi-camera multi-robot system

Here are the conclusions from the authors of this paper:

“The primary contribution and aim of this work is to provide a universal framework for stitching views of the same map from multiple cameras that can be freely moved and laid out around the map, with minimal required configuration. 

The requirements for placement of codes are also loose: only the orientation with respect to the map frame is constrained and configuration of corner codes is required, as well as the lower limit of visible common markers on two images to be processed is 1, with no need for any corner markers to be present in both images at the same time. 

The algorithms efficiency, however, depends on the quality of the homography matrices used in it, which implies that the more detections and corner detections, the better the result. It happens that the stitched / extrapolated coordinates may be off ’ground truth’ in some cases, or even stitching might fail, resulting in malformed output. 

The authors provided experiments on two cameras, yet the algorithm may be run sequentially with images from more cameras. The algorithm may be improved in the future by applying more sophisticated methods of aggregating values of multiple detections of a given robot, such as a weighted combination of the position based on the quality of each detection.”

Project Authors

Artur Morys-Magiera

Artur Morys – Magiera is a PhD candidate at AGH University of Krakow, Poland.

 
 
Marek Długosz

Marek Długosz is a graduate and faculty member of the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at the AGH University of Science and Technology in Krakow, Poland.

Paweł Skruch

Paweł Skruch is a Professor of the AGH University of Science and Technology, Poland.

Learn more

Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

It is modular, customizable and state-of-the-art, and designed to teach, learn, and do research. From exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge, Duckietown evolves with the skills of the user.

Learn about Duckietown
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Intersection Navigation for Duckiebots Using DBSCAN

Duckiebot Intersection Navigation with DBSCAN

Posted on | by Duckietown Admin

Duckiebot Intersection Navigation with DBSCAN

Project Resources
  • Objective: Enable Duckiebots to navigate intersections safely, smoothly and efficiently.
  • Approach: Using the DBSCAN (A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise) algorithm to detect stoplines and guide Duckiebots along precomputed optimal trajectories.
  • Authors: Christian Leopoldseder, Matthias Wieland, Sebastian Seb Giles, Merlin Hosner, Amaury Camus.
Final Result
Code
Authors

Why intersection navigation using DBSCAN?

Navigating intersections is obviously important when driving in Duckietown. It is not as obvious that the mechanics of intersection navigation for autonomous vehicles are very different from those used for standard lane following. There typically is a finite state machine that transitions the agent behavior from one set of algorithms, appropriate for driving down the road, and a different set of algorithms, to actually solve the “intersections” problem. 

The intersection problem in Duckietown has several steps: 

  1. Identifying the beginning of the intersection (identified with a horizontal red line on the road floor)
  2. Stopping at the red line, before engaging the intersection
  3. Identifying what kind of intersection it is (3-way or 4-way, according to the Duckietown appearance specifications at the time of writing)
  4. Identifying the relative position of the Duckiebot at the intersection, hence the available routes forward
  5. Choosing a route
  6. Identifying when it is appropriate to engage the intersection to avoid potentially colliding with other Duckiebots (e.g., is there a centralized coordinator – a traffic light – or not?)
  7. Engaging and navigating the intersection toward the chosen feasible route
  8. Switching the state back to lane following. 

Easier said than done, right?

For each of the points above different approaches could be used. This project focuses on improving the baseline solutions for points 2., and most importantly, 7. of the above.

The real challenge is the actual driving across the intersection (in a safe way, i.e., by “keeping your lane”), because the features that provide robust feedback control in the lane following pipeline are not present inside intersections. The baseline solution for this problem in Duckietown is open loop control, relying on the model of the Duckiebots and the Duckietown to magic-tune a few parameters and the curves just about right. 

As all students of autonomy know, open-loop control is ideally perfect (when all models are known exactly), but it is practically pretty useless on its own, as “all models are wrong” [learn why, e.g., in the Modeling of a Differential Drive robot class]. 

In this project, the authors seek to close the loop around intersection navigation, and chose to use an algorithm called “DBSCAN” (Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise) to do it. 

DBSCAN (Density-Based Spatial Clustering of Applications with Noise – wiki) is a clustering algorithm that groups data points based on density, identifying clusters of varying shapes and filtering out noise. It is used to find the red stop lines at intersections without needing predefined geometric priors (colors, shapes, or fixed positions). This allows to track meaningful visual features in intersections efficiently, localize with respect to them, and hence attempt to navigate along optimal precomputed trajectories depending on the chosen direction.

Learn about planning

Intersection navigation using DBSCAN: the challenges

Some of the challenges in this intersection navigation project are:

Initial position uncertainty: Duckiebot’s starting alignment at the stop line may vary, requiring the system to handle inconsistent initial conditions.

Real-time feedback: the current system lacks real-time feedback, relying on pre-configured instructions that cannot adjust for unexpected events, such as slippage of the wheels, inconsistencies between different Duckiebots, and misalignment of road tiles (non-compliant assembly).

Processing speed: previous closed-loop solution attempts used April tags and Kalman filters – with implementations that ended up being too slow: with low update rates and delays.

Transition to lane following: ensuring a smooth handover from intersection navigation to lane following requires precise control to avoid collisions and lane invasion.

Project Highlights

Here is a visual tour of the output of the authors’ work. Check out the GitHub repository for more details!

Image illustrating Duckiebot navigation options using DBSCAN at an intersection, showing paths for left, right, and straight turns.
Figure 1. Intersection Navigation Options for Duckiebots.
Image showing three Duckiebot alignment scenarios at a stopline: poor alignment on the left and right, with optimal alignment in the center.
Figure 2. Duckiebot alignment relative to the initial stopline for a three-way intersection.
Image of three Duckiebot camera views, each showing different field of view scenarios corresponding to poor, optimal, and poor alignments.
Figure 3. Duckiebot Camera's Field of View Based on Alignment.
Image showing three navigation options for a Duckiebot at an intersection: turning left, turning right, and going straight.
Figure 4. Duckiebot Navigation Possibilities at an Intersection.
Image showing the setup loop for testing Duckiebot intersection navigation performance.
Figure 5. Procedure for Performance Testing of Duckiebot Navigation.
Image illustrating the performance testing loop for Duckiebot's navigation system.
Figure 6. Performance Testing Setup for Duckiebot Navigation.
Image showing fixed intersection, stopline, and Duckiebot coordinate frames used in navigation.
Figure 7. Defined Coordinate Frames for Intersection Navigation.
Comparison of original camera view, rectified camera view, and birdseye view images.
Figure 8. Camera Image Transformation: Original, Rectified, and Birdseye Views.
Visualization of predicted stopline positions and clustering results with colored and white dots.
Figure 9. Clustering and Classification in Duckiebot Navigation.
Visualization of pose estimates from clusters of red pixels with arrows indicating direction and a virtual lane representation.
Figure 10. Stopline Filtering and Pose Estimation in Duckiebot Navigation.
Diagram illustrating the virtual lane in relation to the lane pose during a left turn by the Duckiebot.
Figure 11. Virtual Lane and Lane Pose for Left Turn Navigation.
Diagrams depicting the handover condition and perspectives during the transition back to lane following for the Duckiebot.
Figure 12. Handover Conditions for Lane Following.
Diagram illustrating how the intersection navigation system integrates with the existing Duckiebot architecture.
Figure 13. Integration of Intersection Navigation with the Existing System.
Series of images showing the failed left turn of a Duckiebot due to a missing stopline, across five timesteps.
Figure 14. Sequence of Events Leading to a Failed Left Turn in a Three-Way Intersection.
Sequence of images showing a Duckiebot overshooting a right turn but successfully recovering, across five timesteps.
Figure 15. Overshoot and Recovery During a Right Turn.

Intersection Navigation using DBSCAN: Results

Intersection Navigation using DBSCAN: Authors

Christian Leopoldseder

Christian Leopoldseder is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently works as a Software Engineer at Google, Switzerland.

Matthias Wieland

Matthias Wieland is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently works as a Senior Consultant at abaQon, Switzerland.

Sebastian Nicolas Giles

Sebastian Nicolas Giles is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently works as a Autonomous Driving Systems Engineer at embotech, Switzerland.

Merlin Hosner

Merlin Hosner is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently works as a Process Development Engineer at Climeworks, Switzerland. Merlin was a mentor on this project.

Amaury Camus

Amaury Camus is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently works as a Lead Robotics Engineer at Hydromea, Switzerland. Amaury was a mentor on this project.


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Duckietown is a modular, customizable and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

It is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

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Analysis of Object Detection Models on Duckietown Robot Based on YOLOv5 Architectures

Object Detection on Duckiebots Using YOLOv5 Models

Posted on | by Duckietown Admin
General Information
  • Analysis of Object Detection Models on Duckietown Robot Based on YOLOv5 Architectures
  • Toan-Khoa Nguyen, Lien T. Vu, Viet Q. Vu, TiShu-Hao Liang, en-Dat Hoang, Minh-Quang Tran.
  • National Taiwan University of Science and Technology, Taiwan.
  • Nguyen, T.K., Vu, L.T., Vu, V.Q., Hoang, T.D., Liang, S.H. and Tran, M.Q., 2021. Analysis of object detection models on duckietown robot based on yolov5 architectures. International Journal of iRobotics, 4(4), pp.17-22.
Paper
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Object Detection on Duckiebots Using YOLOv5 Models

Obstacle detection is about having autonomous vehicles perceive their surroundings, identify objects, and determine if they might conflict with the accomplishment of the robot’s task, e.g., navigating to reach a goal position.

Amongst the many applications of AI, object detection from images is arguably the one that experienced the most performance enhancement compared to “traditional approaches” such as color or blob detection. 

Images are, from the point of view of a machine, nothing but (several) “tables” of numbers, where each number represents the intensity of light, at that location, across a channel (e.g., R, G, B for colored images). 

Giving meaning to a cluster of numbers is not as easy as, for a human, it would be to identify a potential obstacle on the path. Machine learning-driven approaches have quickly outperformed traditional computer vision approaches at this task, strong of the abundant and cheap data for training made available by datasets and general imagery on the internet.

Various approaches (networks) for object detection have rapidly succeded in outperforming each other, and YOLO models particularly for their balance of computational efficiency and detection accuracy.  

Learn about robot autonomy, and the difference between traditional and machine learning approaches, from the links below!

Duckietown Educational Resources
Try Object Detection in Duckietown

Abstract

In the author’s words:

Object detection technology is an essential aspect of the development of autonomous vehicles. The crucial first step of any autonomous driving system is to understand the surrounding environment. 

In this study, we present an analysis of object detection models on the Duckietown robot based on You Only Look Once version 5 (YOLOv5) architectures. YOLO model is commonly used for neural network training to enhance the performance of object detection models. 

In a case study of Duckietown, the duckies and cones present hazardous obstacles that vehicles must not drive into. This study implements the popular autonomous vehicles learning platform, Duckietown’s data architecture and classification dataset, to analyze object detection models using different YOLOv5 architectures. Moreover, the performances of different optimizers are also evaluated and optimized for object detection. 

The experiment results show that the pre-trained of large size of YOLOv5 model using the Stochastic Gradient Decent (SGD) performs the best accuracy, in which a mean average precision (mAP) reaches 97.78%. The testing results can provide objective modeling references for relevant object detection studies.

 

Highlights - Object Detection on Duckiebots Using YOLOv5 Models

Here is a visual tour of the work of the authors. For more details, check out the full paper.

 

Conclusion - Object Detection on Duckiebots Using YOLOv5 Models

Here are the conclusions from the authors of this paper:

“This paper presents an analysis of object detection models on the Duckietown robot based on YOLOv5 architectures. The YOLOv5 model has been successfully used to recognize the duckies and cones on the Duckietown. Moreover, the performances of different YOLOv5 architectures are analyzed and compared. 

The results indicate that using the pre-trained model of YOLOv5 architecture with the SGD optimizer can provide excellent accuracy for object detection. The higher accuracy can also be obtained even with the medium size of the YOLOv5 model that enables to accelerate the computation of the system. 

Furthermore, once the object detection model is optimized, it is integrated into the ROS in the Duckietown robot. In future works, it is potential to investigate the YOLOv5 with Layer-wise Adaptive Moments Based (LAMB) optimizer instead of SGD, applying repeated augmentation with Binary Cross-Entropy (BCE), and using domain adaptation technique.”

Yolov5 object detection pic
Figure 1. Duckiebot and Obstacles: Cones and Duckies.
Diagram of YOLOv5 architecture showing backbone, neck, and head sections for object detection.
Figure 2. YOLOv5 Architecture: Backbone, Neck, and Head Components.
Graphs showing training results of YOLOv5s, including coordinate loss, objectness loss, classification loss, precision, and recall.
Figure 3. Training Results of Pre-Trained YOLOv5s for Object Detection.
Bar chart comparing the performance of different YOLOv5 architectures for object detection.
Figure 4. Performance Comparison of YOLOv5 Architectures for Object Detection.

Project Authors

Toan-Khoa Nguyen

Toan-Khoa Nguyen is currently working as an AI engineer at FPT Software AI Center, Vietnam.

 
Lien T. Vu

Lien T. Vu is with the Faculty of Mechanical Engineering and Mechatronics, Phenikaa University, Vietnam.

 
 
Viet Q. Vu

Viet Q. Vu is with the Faculty of International Training, Thai Nguyen University of Technology, Vietnam.

 
 
 
Tien-Dat Hoang

Tien-Dat Hoang is with the Faculty of International Training, Thai Nguyen University of Technology, Vietnam.

 
 
 
Shu-Hao Liang

Shu-Hao Liang is with the Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taiwan.

 
Minh-Quang Tran

Minh-Quang Tran is with the Industry 4.0 Implementation Center, Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taiwan and also with the Department of Mechanical Engineering, Thai Nguyen University of Technology, Vietnam.

 

Learn more

Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

It is modular, customizable and state-of-the-art, and designed to teach, learn, and do research. From exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge, Duckietown evolves with the skills of the user.

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The Obstavoid Algorithm in Duckietown

Obstacle Avoidance for Dynamic Navigation Using Obstavoid

Posted on | by Duckietown Admin

Obstacle Avoidance for Dynamic Navigation Using Obstavoid

Project Resources
  • Objective: Avoiding static and dynamic obstacles in a compliant Duckietown.
  • Approach: Using 3D space-time grid, cost function modeling, and Dijkstra's algorithm.
  • Authors: Alessandro Morra, Dominik Mannhart, Lionel Gulich, Victor Klemm..
Final Result
Code
Authors

Why obstacle avoidance?

The importance of obstacle avoidance in self-driving is self-evident, whether the obstacle is a rubber duckie-pedestrian or another Duckiebot on the road.

In this project, authors deploy the Obstavoid Algorithm aiming to achieve:

  • Safety: preventing collisions with obstacles and other Duckiebots, ensuring safe navigation in a dynamic environment.

  • Efficiency: maintaining smooth movement by optimizing the trajectory, avoiding unnecessary stops or delays.

  • Real-world readiness: preparing Duckietown for real-world scenarios where unexpected obstacles can appear, improving readiness.

  • Traffic management: enabling better handling of complex traffic situations, such as maneuvering around blocked paths or navigating through crowded areas.

  • Autonomous operation: It enhances the vehicle’s ability to operate autonomously, reducing the need for human intervention and improving overall reliability.
obstacle avoidance "obstavoid" project logo
Learn about planning

Obstacle Avoidance: the challenges

Implementing obstacle avoidance in Duckietown introduces the following challenges:

  • Dynamic obstacle prediction: accurately predicting the movement of dynamic obstacles, such as other Duckiebots, to ensure effective avoidance strategies and timely responses.
  • Computational complexity: managing the computational load of the trajectory solver, in “real-time” scenarios with varying obstacle configurations, while ensuring efficient performance on limited computation.
  • Cost function design: creating and fine-tuning a cost function that balances lane adherence, forward motion, and obstacle avoidance, while accommodating both static and dynamic elements in a complex environment.
  • Integration and testing: ensuring integration of the Obstavoid Algorithm with the Duckietown simulation framework and testing its performance in various scenarios to address potential failures and refine its robustness.

The Obstavoid Algorithm addresses these challenges by employing a time-dependent cost grid and Dijkstra’s algorithm for optimal trajectory planning, allowing for “real-time” obstacle avoidance.

Read more about how the Dijkstra’s algorithm is used in this student project titled “Goto-1: Planning with Dijkstra“.

It dynamically calculates and adjusts trajectories based on predicted obstacle movements, ensuring navigation and integration with the simulation framework.

obstacle avoidance with cost functions in Duckietown

Project Highlights

Here is the output of the authors’ work. Check out the GitHub r epository for more details!

 
3D cost grid illustration depicting a weighted space-time grid, used for shortest path optimization in the Obstavoid Algorithm.
Figure 1. 3D Cost Grid Illustration for Obstavoid Algorithm.
Graph showing a static cost function with a 6th-degree polynomial curve, representing lane following and forward motion in the Obstavoid Algorithm.
Figure 2. Static Cost Function for Lane Following and Forward Motion.
Flowchart illustrating the software architecture of the Obstavoid Algorithm, detailing two main nodes: the trajectory creator node and the trajectory sampler node, and their communication with the simulation.
Figure 3. Software Architecture of the Obstavoid Algorithm.
Graph showing the performance of the trajectory solver over 100 trajectory generations. The graph displays variability in solution times due to different obstacle configurations in the cost grid.
Figure 4. Performance Analysis of Trajectory Solver.

Obstacle Avoidance: Results

Obstacle Avoidance: Authors

Alessandro Morra

Alessandro Morra is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently serves as the CEO & Co-Founder at Ascento, Switzerland.

 
 
Dominik Mannhart

Dominik Mannhart is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently serves as the Co-Founder at Ascento, Switzerland.

 
Lionel Gulich

Lionel Gulich is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently works as a Senior Robotics Software Engineer at NVIDIA, Switzerland.

 
 
Victor Klemm

Victor Klemm is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently is a PhD student at Robotics Systems Lab, ETH Zurich, Switzerland.

 
 
Dzenan Lapandic

Dženan Lapandić is a former Duckietown student and teaching assistant of the Autonomous Mobility on Demand class at ETH Zurich, and currently is a PhD candidate at KTH Royal Institute of Technology, Sweden.

 

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Duckietown is a modular, customizable and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

It is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

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Duckietown Access Remotely high res

ProTip: Duckiebot Remote Connection

Posted on | by Duckietown Admin

ProTip: Duckiebot Remote Connection

Have you ever wanted to work from home, but your robot is in the lab? Networks are notoriosly the trickyest aspect of robotics, and establishing a Duckiebot remote connection can be a real challenge. 

The good news is, that as long as your Duckiebot has been left powered on, it is possible to establish a Duckiebot remote connection and operate the robot as if you were on the same network. 

In this guide, we will show how to access your Duckiebot from anywhere in the world using ZeroTier.

Duckiebot remote control setup instructions

ProTips

Knowing the science does not necessarily mean being practical with the tips and tricks of the roboticist job. “ProTips” are professional tips discussing (apparently) “small details” of the everyday life of a roboticist.

We collect these tips to create a guideline for “best practices”, whether for saving time, reducing mistakes, or getting better performances from our robots. The objective is to share professional knowledge in an accessible way, to make the life of every roboticist easier! 

If you would like to contribute a ProTip, reach out.

About Duckietown

Duckietown is a platform that streamlines teaching, learning, and doing research on robot autonomy by offering hardware, software, curricula, technical documentation, and an international community for learners.

Check out the links below to learn more about Duckietown and start your learning or teaching adventure.

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Survey on Testbeds for Vehicle Autonomy & Robot Swarms

Survey on Testbeds for Vehicle Autonomy & Robot Swarms

Posted on | by Duckietown Admin
General Information
  • A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms
  • Armin Mokhtarian, Jianye Xu, Patrick Scheffe, Maximilian Kloock, Simon Schäfer, Heeseung Bang, Viet-Anh Le, Sangeet Ulhas, Johannes Betz, Sean Wilson, Spring Berman, Liam Paull, Amanda Prorok, Bassam Alrifaee
  • RWTH Aachen University, Germany
  • Mokhtarian, Armin & Scheffe, Patrick & Kloock, Maximilian & Schäfer, Simon & Bang, Heeseung & Le, Viet-Anh & Sankaramangalam Ulhas, Sangeet & Betz, Johannes & Wilson, Sean & Berman, Spring & Prorok, Amanda & Alrifaee, Bassam. (2024). A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms. 10.13140/RG.2.2.16176.74248/1.
Paper
Institution
Authors
CPM Website

Survey on Testbeds for Vehicle Autonomy & Robot Swarms

Collage showcasing diverse testbeds in the realm of Connected and Automated Vehicles, Vehicle Autonomy and Robot Swarms

“A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms“ by Armin Mokhtarian et al. offers a comparison of current small-scale testbeds for Connected and Automated Vehicles (CAVs), Vehicle Autonomy and Robot Swarms (RS).

As mentioned in , small-scale autonomous vehicle testbeds are paving the way to faster and more meaningful research and development in vehicle autonomy, embodied AI, and AI robotics as a whole. 

Although small-scale, often made of off-the-shelf components and relatively low-cost, these platforms provide the opportunity for deep insights into specific scientific and technological challenges of autonomy. 

Duckietown, in particular, is highlighted for its modular, miniature-scale smart-city environment, which facilitates the study of autonomous vehicle localization and traffic management through onboard sensors.

Learn about robot autonomy, traditional robotics autonomy architectures, agent training, sim2real, navigation, and other topics with Duckietown, starting from the link below!

Duckietown Educational Resources

Abstract

Connected and Automated Vehicles (CAVs) and Robot Swarms (RS) have the potential to transform the transportation and manufacturing sectors into safer, more efficient, sustainable systems.

However, extensive testing and validation of their algorithms are required. Small-scale testbeds offer a cost-effective and controlled environment for testing algorithms, bridging the gap between full-scale experiments and simulations. This paper provides a structured overview of characteristics of testbeds based on the sense-plan-act paradigm, enabling the classification of existing testbeds.

Its aim is to present a comprehensive survey of various testbeds and their capabilities. We investigated 17 testbeds and present our results on the public webpage www.cpm-remote.de/testbeds.

Furthermore, this paper examines seven testbeds in detail to demonstrate how the identified characteristics can be used for classification purposes.

Highlights - Survey on Testbeds for Vehicle Autonomy & Robot Swarms

Here is a visual tour of the authors’ work. For more details, check out the full paper or the corresponding up-to-date project website.

 
Collage of various small-scale testbeds for Connected and Automated Vehicles (CAVs), Vehicle Autonomy and Robot Swarms, highlighting different setups and testing environments.
Figure 1. Collage showcasing diverse testbeds in the realm of Connected and Automated Vehicles and Robot Swarms.
Screenshot of a webpage displaying a list of testbeds for Connected and Automated Vehicles and Robot Swarms, as investigated in a research study.
Figure 2. Screenshot of Public Webpage Listing Investigated Testbeds in Connected Vehicles & Robot Swarms Study.
Cyber-Physical Mobility Lab at RWTH Aachen University, featuring testbeds for research on Connected and Automated Vehicles and Robot Swarms.
Figure 3. Cyber-Physical Mobility Lab at RWTH Aachen University.
IDS Scaled Smart City testbed at Cornell University, designed for research in connected vehicles and smart city technologies.
Figure 4. IDS Scaled Smart City at Cornell University.
Robotarium testbed at Georgia Institute of Technology, featuring multiple small robots in a collaborative swarm setup.
Figure 5. Robotarium Testbed at Georgia Institute of Technology.
Cambridge Minicars testbed at the Prorok Lab, Cambridge University, showcasing miniature vehicles for multi-agent systems research.
Figure 6. Cambridge Minicars at the Prorok Lab at Cambridge University.
Go-CHART testbed at Arizona State University, featuring scaled autonomous vehicles for testing control and coordination strategies.
Figure 7. The Go-CHART at Arizona State University.
F1TENTH vehicle built at the University of Pennsylvania, a scaled-down autonomous race car used for research and competitions.
Figure 8. An exemplar F1TENTH vehicle built at the University of Pennsylvania.
Duckietown testbed at MIT, featuring miniature autonomous vehicles navigating a small-scale city environment with roads and traffic signs.
Figure 9. Duckietown at Massachusetts Institute of Technology.

Conclusion - Survey on Testbeds for Vehicle Autonomy & Robot Swarms

Here are the conclusions from the authors of this paper:

“This survey provides a detailed overview of small-scale CAV/RS testbeds, with the aim of helping researchers in these fields to select or build the most suitable testbed for their experiments and to identify potential research focus areas. We structured the survey according to characteristics derived from potential use cases and research topics within the sense-plan-act paradigm.

Through an extensive investigation of 17 testbeds, we have evaluated 56 characteristics and have made the results of this analysis available on our webpage. We invited the testbed creators to assist in the initial process of gathering information and updating the content of this webpage. This collaborative approach ensures that the survey maintains its relevance and remains up to date with the latest developments.

The ongoing maintenance will allow researchers to access the most recent information. In addition, this paper can serve as a guide for those interested in creating a new testbed. The characteristics and overview of the testbeds presented in this survey can help identify potential gaps and areas for improvement.

One ongoing challenge that we identified with small-scale testbeds is the enhancement of their ability to accurately map to realworld conditions, ensuring that experiments conducted are as realistic and applicable as possible.

Overall, this paper provides a resource for researchers and developers in the fields of connected and automated vehicles and robot swarms, enabling them to make informed decisions when selecting or replicating a testbed and supporting the advancement of testbed technologies by identifying research gaps.”

Project Authors

Armin Mokhtarian

Armin Mokhtarian is currently working as a Research Associate & PhD Candidate at RWTH Aachen University, Germany.

 
Patrick Scheffe

Patrick Scheffe is a Research Associate at Lehrstuhl Informatik 11 – Embedded Software, Germany.

 
Maximilian Kloock

Maximilian Kloock is working as a Team Manager Advanced Battery Management System Technologies at FEV Europe, Germany.

Simon Schäfer

Simon Schäfer is a Visiting Researcher at Faculty of Engineering, University of Alberta, Canada.

Heeseung Bang

Heeseung Bang is currently a Postdoctoral Associate at Cornell University, USA.

 
Viet-Anh Le

Viet-Anh Le is a Visiting Graduate Student at Cornell University, USA.

Sangeet Ulhas

Sangeet Ulhas is a PhD candidate at Ira A. Fulton Schools of Engineering at Arizona State University, USA.

Johannes Betz

Johannes Betz is a Assistant Professor at Technische Universität München, Germany.

 
Sean Wilson

Sean Wilson is a Senior Research Engineer at Georgia Institute of Technology, USA.

 
Spring Berman

Spring Berman is an Associate Professor at Arizona State University, USA.

Liam Paull

Liam Paull is an Associate Professor at Université de Montréal, Canada and he is also the Chief Education Officer at Duckietown, USA.

 
Amanda Prorok

Amanda Prorok is an associate professor at University of Cambridge, UK.

 
Bassam Alrifaee

Bassam Alrifaee is a Professor at Bundeswehr University Munich, Germany.

Learn more

Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

It is modular, customizable and state-of-the-art, and designed to teach, learn, and do research. From exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge, Duckietown evolves with the skills of the user.

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Monocular Visual Odometry

Monocular Visual Odometry for Duckiebot Navigation

Posted on | by Duckietown Admin

Monocular Visual Odometry for Duckiebot Navigation

Project Resources
  • Objective: Obtaining a "real-time" estimate of the pose of a Duckiebot traveling in Duckietown.
  • Approach: Using monocular visual odometry.
  • Authors: Gianmarco Bernasconi, Tomasz Firynowicz, Guillem Torrente, Yang Liu.
Final Result
Code
Authors

Why Monocular Visual Odometry?

Monocular Visual Odometry (VO) falls under the “perception” block of the traditional robot autonomy architecture. 

Perception in robot autonomy involves transforming sensor data into actionable information to accomplish a given task in the environment.

Perception is crucial because it allows robots to create a representation of themselves in teh environment they are operating within, which in turn enables the robot to navigate, avoid static or dynamic obstacles, forming the foundation for effective autonomy.

The function of monocular visual odometry is to estimate the robot’s pose over time by analyzing the sequence of images captured by a single camera. 

VO in this project is implemented through the following steps:

  1. Image acquisition: the node receives images from the camera, which serve as the primary source of data for motion estimation.

  2. Feature extraction: key features (points of interest) are extracted from the images using methods like ORB, SURF, or SIFT, which highlight salient details in the scene.

  3. Feature matching: the extracted features from consecutive images are matched, identifying how certain points have moved from one image to the next.

  4. Outlier filtering: erroneous or mismatched features are filtered out, improving the accuracy of the feature matches. In this project, histogram fitting to discard outliers is used.

  5. Rotation estimation: the filtered feature matches are used to estimate the rotation of the Duckiebot, determining how the orientation has changed.

  6. Translation estimation: simultaneously, the node estimates the translation, i.e., how much the Duckiebot has moved in space.

  7. Camera information and kinematics inputs: additional information from the camera (e.g., intrinsic parameters) and kinematic data (e.g., velocity) help refine the translation and rotation estimations.

  8. Path and odometry outputs: the final estimated motion is used to update the Duckiebot’s odometry (evolution of pose estimate over time) and the path it follows within the environment.

Monocular visual odometry is challenging, but provide low-cost, camera-based solution for real-time motion estimation in dynamic environments.

Write and test your own visual odometry algorithm

Monocular Visual Odometry: the challenges

Implementing Monocular Visual Odometry involves processing images at runtime, presents challenges that effect performance.
  • Extracting and matching visual features from consecutive images is a fundamental task in monocular VO. This process can be hindered by factors such as low texture areas, motion blur, variations in lighting conditions and occlusions.
  • Monocular VO systems face inherent scale ambiguity since a single camera cannot directly measure depth. The system must infer scale from visual features, which can be error-prone and less accurate in the absence of depth cues.
  • Running VO algorithms requires significant computational resources, particularly when processing high-resolution images at a high frequency. The Raspberry Pi used in the Duckiebot has limited processing power and memory, which contrians the performance of the visual odometry pipeline (the newer Duckiebots, DB21J uses Jetson Nano for computation.)
  • Monocular VO systems, as all odometry systems relying on dead-recokning models, are susceptible to long-term drift and divergence due to cumulative errors in feature tracking and pose estimation.
This project addresses visual odometry challenges by implementing robust feature extraction and matching algorithms (ORB by default) and optimizing parameters to handle dynamic environments and computational constraints. Moreover, it integrates visual odometry with existing Duckiebot autonomy pipeline, leveraging the finite state machine for accurate pose estimation and navigation.

Project Highlights

Here is the output of the authors’ work. Check out the GitHub repository for more details!

 
Image showing the estimated path of the Duckiebot during the visual odometry demo.
Figure 1. Path During Visual Odometry Demo.
Image showing the process of removing outliers in visual odometry feature matching using histogram fitting.
Figure 2. Outlier Removal Using Histogram Fitting.
Image illustrating the division of feature pairs into far and close regions for motion estimation in visual odometry.
Figure 3. Region Division for Motion Estimation.
Block diagram showing the steps of the visual odometry pipeline for Duckiebot pose estimation.
Figure 4. Block Diagram of Visual Odometry Implementation.

Monocular Visual Odometry: Results

Monocular Visual Odometry: Authors

Gianmarco Bernasconi

Gianmarco Bernasconi is a former Duckietown student of class Autonomous Mobility on Demand at ETH Zurich, and currently works as a Senior Research Engineer at Motional, Singapore.

 
Tomasz Firynowicz

Tomasz Firynowicz is a former Duckietown student and teaching assistant of the Autonomous Mobility on Demand class at ETH Zurich, and currently works as a Software Engineer at Dentsply Sirona, Switzerland. Tomasz was a mentor on this project.

 
Guillem Torrente Martí

Guillem Torrente Martí is a former Duckietown student and teaching assistant of the Autonomous Mobility on Demand class at ETH Zurich, and currently works as a Robotics Engineer at SonyAI, Japan. Guillem was a mentor on this project.

Yang Liu

Yang Liu is a former Duckietown student and teaching assistant of the Autonomous Mobility on Demand class at ETH Zurich, and currently is a Doctoral Student at EPFL, Switzerland. Yang was a mentor on this project

Learn more

Duckietown is a modular, customizable and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

It is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

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