TY - GEN
T1 - Unsupervised and online non-stationary obstacle discovery and modeling using a laser range finder
AU - Duceux, G.
AU - Filliat, D.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/31
Y1 - 2014/10/31
N2 - 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.
AB - 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.
U2 - 10.1109/IROS.2014.6942620
DO - 10.1109/IROS.2014.6942620
M3 - Conference contribution
AN - SCOPUS:84911479057
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 593
EP - 599
BT - IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
Y2 - 14 September 2014 through 18 September 2014
ER -