VISUAL-BASED DETECTION AND TRACKING OF DYNAMIC OBSTACLES FROM A MOBILE ROBOT

This paper presents a methodology for detecting and tracking moving objects during mobile robot navigation in unknown environments using only visual information. An initial set of interest points is detected arid then tracked by the Kattade-Lucas tracker (KLT). Along few images, point positions and velocities are accumulated and a spatio-temporal analysis, based on the a contrario theory, is performed for the clustering process of these points. All dynamical sets of points found by the clustering are directly initialized and tracked as moving objects using Kalman Filter. At each image, a probability map saves temporally the previous interesting point positions with a certain probability value. New features will be added in the most likely zones based on this probability map. The process detection-clustering-tracking is executed in an iterative way to guarantee the detection of new moving objects or to incrementally enlarge already detected objects until their real limits. Experimental results on real dynamic images acquired during robot outdoor and indoor navigation task are presented. Furthermore, rigid and non rigid moving object tracking results are compared and discussed.