Recognizing Moving Obstacles for Robot Navigation using Real-time Omnidirectional Stereo Vision

We describe a method to recognize moving obstacles in a wide view and in real-time. Such an ability is required when a mobile robot moves in a dynamic environment. Our method uses an omnidirectional stereo vision which is composed of a pair of vertically-aligned omnidirectional cameras and a PC cluster to obtain panoramic range information of 360 degrees in real-time. From this range information, the robot on-line generates a free space map of the surrounding environment, and extracts objects in the free space as candidates for moving obstacles. The robot makes temporal correspondence of the candidates and estimates their position and velocity using the Kalman filter. To reduce the effect of odometry error to map generation, the ego-motion is estimated by comparing the current and previous range data. We demonstrate the effectiveness of our method by on-line experiments.