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The problems of vision-based detection and tracking of independently moving objects, localization and map construction are highly interrelated, in the sense that the solution of any of them provides valuable information to the solution of the others. In this paper, rather than trying to solve each of them in isolation, we propose a method that treats all of them simultaneously. More specifically, given visual input acquired by a moving RGBD camera, the method detects independently moving objects and tracks them in time. Additionally, the method estimates the camera (ego)motion and the motion of the tracked objects in a coordinate system that is attached to the static environment, a map of which is progressively built from scratch. The loose assumptions that the method adopts with respect to the problem parameters make it a valuable component for any robotic platform that moves in a dynamic environment and requires simultaneous tracking of moving objects, egomotion estimation and map construction. The usability of the method is further enhanced by its robustness and its low computational requirements that permit real time execution even on low-end CPUs.

Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform