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.

The new planning capailities of Duckiebots enable autonomous navigation building on pre-existing functionalities, such as “lane following”, “intersection detection and identification”, and “intersection navigation” (we are operating in a scenario with only one agent on the map, so coordination and obstacle avoidance are not central to this progect).

Lane following in Duckietown is mainly vision-based, and as such suffers from the typical challenges of vision in robotics: motion blur, occlusions, sensitivity to environmental lighting conditions and “slow” sampling.

Intersection detection in Duckietown relies on the identification of the red lines on the road layer. Identification of the type of intersection, and relative location of the Duckiebot with respect to it, is instead achieved through the detection and interpretation of fiducial markers, appropriately specified and located on the map. In the case of Duckietown, April Tags (ATs) are used. Each AT, in addition to providing the necessary information regarding the type of intersection (3- or 4-way) and the position of the Duckiebot with respect to the intersection, is mapped to a unique ID in the Duckietown traffic sign database. 

These traffic signs IDs can be used to unamiguosly define the graph of the city roads. Based on this, and leveraging the lane following pipeline state estimator, it is possible to estimate the location (with tile accuracy) of the Duckiebot with respect to a global map reference frame, hence providing the agent sufficient information to know when to stop.

After stopping at an intersection, detecting and identifying it, Duckiebots are ready to choose which direction to go next. This is where the Dijkstra planning algorithm comes into play. After the planner communicates the desired turn to take, the Duckiebot drives through the intersection, before switchng back to lane following behavior after completing the crossing. In Duckietown, we refer to the combined operation of these states as “indefinite navigation”. 

Switching between different “states” of the robot mind (lane following, intersection detection and identification, intersection navigation, and then back to lane following) requires the careful design and implementation of a “finite state machine” which, triggered by specific events, allows for the Duckiebot to transition between these states. 

Integrating a new package within the existing indefinite navigation framework can cause inconsistencies and undefined behaviors, including unreliable AT detection, lane following difficulties, and inconsistent intersection navigation.

Performance evaluation of the GOTO-1 project involved testing three implementations with ten trials each, revealing variability in success rates.

Some of the key challenges associated with vision-based object detection are the following:

Robustness across variable lighting conditions: Ensuring accurate object detection under diverse lighting is complex due to changes in object appearance (check out why in our computer vision classes). The model must handle different lighting scenarios effectively.

Balancing robustness and performance: There’s a trade-off between robustness to lighting variations and achieving high accuracy in standard operating conditions. Prioritizing one may affect the other.

Integration and real-time performance: Integrating the trained neural network (NN) model into the Duckiebot’s system is required for real-time operation, avoid lags associated with transport of images across networks. The model’s complexity therefore must align with the computational resources available. This project was executed on DB19 model Duckiebots, equipped with Raspberry Pi 3B+ and a Coral board.

Data quality and generalization: Ensuring the model generalizes well despite potential biases in the training dataset and transfer learning challenges is crucial. Proper dataset curation and validation are essential.

Some of the key challenges associated with this project are the following:

Detection Accuracy: The Duckiebot and Duckies detection systems occasionally produce false positives. Light sources from other Duckiebots or shiny objects can interfere with the LED detection, while yellow line segments can be mistaken for Duckies. Improving the reliability of detection under varying lighting conditions is essential.

Lane Following Stability: The Duckiebots sometimes become unstable while overtaking, especially when driving in the left lane. The lane-following system struggles with large lane pose angles or rapid changes in lane position, which can cause the Duckiebot to veer off the road. Enhancing the lane-following algorithm to maintain stability during lane changes is critical.

Velocity Estimation: Estimating the speed of moving Duckiebots accurately is challenging. The current position data obtained from LED detection fluctuates too much to provide a reliable velocity measurement. Developing a more robust method for estimating the velocity of other Duckiebots is needed to ensure safe and efficient overtaking.

Variable Speed Control: Implementing variable speed control during overtaking is problematic due to instability in the lane-following pipeline when speeds are dynamically adjusted. Adjusting speed based on the detected obstacle’s speed without losing lane stability is difficult, necessitating improvements in the lane control model to handle speed changes effectively.