Unsupervised and online non-stationary obstacle discovery and modeling using a laser range finder

• Published in: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 593 - 599
• Main Authors: Duceux, G., Filliat, D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2014
• Subjects: Computational modeling; Laser modes; Robot sensing systems; Shape; Three-dimensional displays; Trajectory
• ISSN: 2153-0858; 2153-0866
• DOI: 10.1109/IROS.2014.6942620

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.