Vision-based 3-D trajectory tracking for unknown environments

TitleVision-based 3-D trajectory tracking for unknown environments
Publication TypeJournal Article
Year of Publication2006
AuthorsSaeedi, P., P. D. Lawrence, and D. G. Lowe
JournalRobotics, IEEE Transactions on
Pagination119 - 136
Date Publishedfeb.
Keywords3D localization, complete error-propagation modeling scheme, covariance matrices, covariance matrix, extra sensory devices, iterative methods, iterative motion estimation, Kalman filtering, Kalman filters, mobile robot, mobile robots, motion estimation, mountable head, onboard charge-coupled device cameras, path planning, prior scene knowledge, robot vision, stereo algorithm, two-stage feature tracking, unknown environments, vision-based 3D trajectory tracking

This paper describes a vision-based system for 3-D localization of a mobile robot in a natural environment. The system includes a mountable head with three on-board charge-coupled device cameras that can be installed on the robot. The main emphasis of this paper is on the ability to estimate the motion of the robot independently from any prior scene knowledge, landmark, or extra sensory devices. Distinctive scene features are identified using a novel algorithm, and their 3-D locations are estimated with high accuracy by a stereo algorithm. Using new two-stage feature tracking and iterative motion estimation in a symbiotic manner, precise motion vectors are obtained. The 3-D positions of scene features and the robot are refined by a Kalman filtering approach with a complete error-propagation modeling scheme. Experimental results show that good tracking and localization can be achieved using the proposed vision system.


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