Moving Obstacles Detection and Camera Pointing for Mobile Robot Applications

International audience; Perception is one of the most important tasks for safe navigation of autonomous mobile robots in outdoor environments that are dynamic and unknown. This paper presents a real time system for detection and classification of dynamic objects using a 2D laser range finder (2D LRF) and a moving camera mounted on the mobile robot. An occupancy grid based detection is implemented using 2D LRF and moving objects are iteratively extracted from the global occupancy grid. A visual classification system is developed using HOG features and AdaBoost as learning process. Dynamic objects detection and visual classification results are coupled to point moving camera on the specified target (specified by mission supervisor) and to identify it. Finally, real time experiments results on the mobile robot in two different scenarios are presented.