This paper describes an ego-motion estimation method by integrating multiple scan matching results. The method considers both the uncertainty of scan matching results and that of estimated ego-motions, and not only estimates the latest ego-motion but also updates previous ego-motions. The estimation process is formulated as an iterative one using Kalman filter. We implement the method by using an omnidirectional stereo-based scan matching method. Experimental results show the effectiveness of the proposed method.

This paper describes a mobile robot navigation method in dynamic environments. The method uses a real-time omnidirectional stereo, which can obtain panoramic range information of 360 degrees. From this panoramic range information, the robot estimates its ego-motion by comparing the current and the previous observations in order to integrate observations obtained at different positions. The uncertainty in the estimation is also calculated. Next, the robot recognizes and tracks moving obstacles. Finally, the robot plans a collision free path by a heuristic planner in space-time considering the velocity uncertainty of observed obstacles. Experimental results show the effectiveness of our method.

This paper describes a real-time omnidirectional stereo system and its application to obstacle detection and tracking for a mobile robot. The stereo system uses two omnidirectional cameras aligned vertically. The images from the cameras are converted into panoramic images, which are then examined for stereo matching along vertical epipolar lines. A PC cluster system composed of 6 PCs can generate omnidirectional range data of 720/spl times/100 pixels with disparity range of 80 (about 5 frames per second). For obstacle detection, a map of static obstacles is first generated. The candidates for moving obstacles are then extracted by comparing the current observation with the map. The temporal correspondence between the candidates are established based on their estimated position and velocity which are calculated using Kalman filter-based tracking. Experimental results for a real scene are described.