Visual Obstacle Detection using Inverse Perspective Mapping

Visual Obstacle Detection using Inverse Perspective Mapping

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Visual Obstacle Detection using Inverse Perspective Mapping

Project Resources
  • Objective: To develop a visual obstacle detection system for Duckiebots using inverse perspective mapping to improve navigation accuracy and safety.
  • Approach: Employ inverse perspective mapping to transform monocular camera inputs into Bird’s Eye View representations, enabling reliable obstacle detection and classification.
  • Authors: Julian Nubert, Niklas Funk, Fabio Meier, Fabrice Oehler
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Project highlights

Here is a visual tour of the authors’ work on implementing visual obstacle detection in Duckietown.

Monocular camera image capturing a Duckiebot's perspective of the road for visual obstacle detection.
Figure 1. Example Image from Monocular Camera.
Bird’s Eye View image created from monocular camera input using inverse perspective mapping.
Figure 2. Image Transformed to Bird’s Eye View.
Final detection output showing identified obstacles in Bird’s Eye View.
Figure 3. Final Detection Output.
Cropped version of the monocular camera input, focusing on relevant road sections.
Figure 4. Cropped Image for Efficient Detection.
Bird’s Eye View showing detected obstacle boxes overlayed on the road.
Figure 5. Display of Obstacle Boxes in Bird’s Eye View.
Detected obstacle in Bird’s Eye View with position and radius annotations.
Figure 6. Position and Radius of Obstacle.
Bird’s Eye View image classifying obstacles as dangerous or non-dangerous based on their position.
Figure 7. Dangerous vs. Non-Dangerous Obstacles.
Search lines in Bird’s Eye View used to determine if white lane boundaries lie between the Duckiebot and an obstacle.
Figure 8. Search Lines for Lane Boundary Detection.
Flowchart showing initial logic stages for obstacle handling during commissioning.
Figure 9. Initial Logic Stages for Commissioning.
Definitions of variables in obstacle detection, as seen from the top view.
Figure 10. Top-View Variable Definitions.
Geometry depicting the Duckiebot’s path and an obstacle's position in the lane.
Figure 11. Geometry of Scene and Obstacle Positioning.
Diagram showing the software architecture for obstacle detection and avoidance in Duckietown.
Figure 12. Software Architecture Overview.
Example of obstacle detection error caused by motion blur.
Figure 13. Motion Blur Impact on Obstacle Detection.
Example of an adaptive bounding box conforming to lane curvature for improved obstacle detection.
Figure 14. Adaptive Bounding Box for Lane Curvature.

Visual Obstacle Detection: objective and importance

This project aims to develop a visual obstacle detection system using inverse perspective mapping with the goal to enable autonomous systems to detect obstacles in real time using images from a monocular RGB camera. It focuses on identifying specific obstacles, such as yellow Duckies and orange cones, in Duckietown.

The system ensures safe navigation by avoiding obstacles within the vehicle’s lane or stopping when avoidance is not feasible. It does not utilize learning algorithms, prioritizing a hard-coded approach due to hardware constraints. The objective includes enhancing obstacle detection reliability under varying illumination and object properties.

It is intended to simulate realistic scenarios for autonomous driving systems. Key metrics of evaluation were selected to be detection accuracy, false positives, and missed obstacles under diverse conditions. 

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The method and the challenges visual obstacle detection using Inverse Perspective Mapping

The system processes images from a monocular RGB camera by applying inverse perspective mapping to generate a bird’s-eye view, assuming all pixels lie on the ground plane to simplify obstacle distortion detection. Obstacle detection involves HSV color filtering, image segmentation, and classification using eigenvalue analysis. The reaction strategies include trajectory planning or stopping based on the detected obstacle’s position and lane constraints.

Computational efficiency is a significant challenge due to the hardware limitations of Raspberry Pi, necessitating the avoidance of real-time re-computation of color corrections. Variability in lighting and motion blur impact detection reliability, while accurate calibration of camera parameters is essential for precise 3D obstacle localization. Integration of avoidance strategies faces additional challenges due to inaccuracies in pose estimation and trajectory planning.

Visual Obstacle Detection using Inverse Perspective Mapping: Full Report

Visual Obstacle Detection using Inverse Perspective Mapping: Authors

Julian Nubert

Julian Nubert is currently a Research Assistant & Doctoral Candidate at the Max Planck Institute for Intelligent Systems, Germany.

Niklas Funk

Niklas Funk is a PHD Graduate Student at Technische Universität Darmstadt, Germany.

Fabio Meier

Fabio Meier is currently working as the Head of Operational Data Intelligence at Sensirion Connected Solutions, Switzerland.

Fabrice Oehler

Fabrice Oehler is working as a Software Engineer at Sensirion, Switzerland.

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

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

These spotlight projects are shared to exemplify Duckietown’s value for hands-on learning in robotics and AI, enabling students to apply theoretical concepts to practical challenges in autonomous robotics, boosting competence and job prospects.

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