Moving Obstacles Detection and Camera Pointing for Mobile Robot Applications

Mustapha Lakrouf (1, 2, 3) , Stanislas Larnier (4) , Michel Devy (1) , Nouara Achour (2)

1 LAAS-RAP - Équipe Robotique, Action et Perception
2 LRPE - Laboratoire de Robotique Parallélisme Electroénergétique [Alger]
3 cdta - Centre de devellopement des technologies avancées
4 AKKA Technologies

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.

Mots clés: Occupancy grid; moving objects detection; visual; classification and pedestrian detection

Domaines: Informatique [cs]; Robotique [cs.RO]

Mustapha Lakrouf, Stanislas Larnier, Michel Devy, Nouara Achour. Moving Obstacles Detection and Camera Pointing for Mobile Robot Applications. ICMRE 2017 2017 3rd International Conference on Mechatronics and Robotics Engineering , Feb 2017, Paris, France. 6p., ⟨10.1145/3068796.3068816⟩. ⟨hal-01579420⟩