Using laser range finders has shown its efficiency to perform mapping and navigation for mobile robots. However, most of existing methods assume a mostly static world and filter away dynamic aspects while those dynamic aspects are often caused by non-stationary objects which may be important for the robot task. We propose an approach that makes it possible to detect, learn and recognize these objects through a multi-view model, using only a planar laser range finder. We show using a supervised approach that despite the limited information provided by the sensor, it is possible to recognize efficiently up to 22 different object, with a low computing cost while taking advantage of the large field of view of the sensor. We also propose an online, incremental and unsupervised approach that make it possible to continuously discover and learn all kind of dynamic elements encountered by the robot including people and objects.

This paper demonstrates accurate micropositioning scheme based on a direct visual servoing process. This technique uses only the pure image signal (photometric information) to design the control law. With respect to traditional visual servoing approaches that use geometric visual features (points, lines ...), the visual features used in the control law are the pixel intensity. The proposed approach was tested in term of accuracy and robustness in several experimental conditions. The obtained results have demonstrated a good behavior of the control law and very good positioning accuracy. The obtained accuracies are estimated to 14 nm, 89 nm, and 0.001 degrees in the x, y and θ axes of positioning platform, respectively.