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.

/pdf/real-time-omnidirectional-stereo-for-obstacle-detection-and-12ql9i17nb.pdf

Application of Kalman Filter

Real-time omnidirectional stereo for obstacle detection and tracking in dynamic environments

We describe a method to recognize moving obstacles in a wide view and in real-time. Such an ability is required when a mobilerobot moves in a dynamic environment. Our method uses an omnidirectional stereo vision which is composed of a pair ofvertically-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 objectsin the free space as candidates for moving obstacles. The robot makes temporal correspondence of the candidates and estimatestheir position and velocity using the Kalman ﬁlter. To reduce the eﬀect of odometry error to map generation, the ego-motionis estimated by comparing the current and previous range data. We demonstrate the eﬀectiveness of our method by on-lineexperiments.

/pdf/recognizing-moving-obstacles-for-robot-navigation-using-real-39h21606xc.pdf

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

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.

/pdf/mobile-robot-navigation-in-dynamic-environments-using-xzzm4o5qki.pdf

Mobile robot navigation in dynamic environments using omnidirectional stereo

A new adaptive estimation method of spacecraft thermal mathematical model with an ensemble Kalman filter

This bibliography on the Kalman filter was obtained from the Cornell University Library Resources computer network. The four indexes used were Agricola, Biosis pre-1993, Biosis post-1992, and CAB. The references to Kalman filtering found in these indexes are listed together as one bibliography in Section 2 of the paper. Identification of content for each citation is listed after the reference. The Current Index in Statistics was used to obtain the bibliography given in Section 3 . By not combining the Current Index with the others, it is possible to observe the completeness of the Current Index relative to the others. References in the physical sciences fields for the most part are not included in this bibliography unless they appeared in these indexes. References on applications for the most part come from the ·fields of agriculture, biology, and medicine.

/pdf/kalman-filter-bibliography-agriculture-biology-and-medicine-2gcah6s8c6.pdf

Kalman Filter Bibliography: Agriculture, Biology, and Medicine

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0266466600010392

Application of Kalman Filter

Citations

Real-time omnidirectional stereo for obstacle detection and tracking in dynamic environments

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

Mobile robot navigation in dynamic environments using omnidirectional stereo

A new adaptive estimation method of spacecraft thermal mathematical model with an ensemble Kalman filter

Kalman Filter Bibliography: Agriculture, Biology, and Medicine

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