“Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive results in computer vision, most of the previous obstacle detection works only leverage traditional stereo matching techniques to meet the computational constraints for real-time feedback. This paper proposes a computationally efficient method that employs a deep neural network to detect occupancy from stereo images directly. … Our approach extracts the compact obstacle distribution based on volumetric representations.”

Find the paper and full list of authors in the 2023 IEEE International Conference on Robotics and Automation proceedings.