TY - GEN
T1 - DSM update for robust outdoor localization using ICP-based scan matching with COAG features of laser range data
AU - Ji, Yong Hoon
AU - Hong, Sung Ho
AU - Song, Jae Bok
AU - Choi, Ji Hoon
PY - 2011
Y1 - 2011
N2 - Localization of a mobile robot is a very important task for autonomous navigation. However, with only an initially inaccurate map, a mobile robot cannot estimate its pose robustly because of the inconsistency between the real observations from the environment and the predicted observations on the inaccurate map. The main map used for outdoor environment is DSM (Digital Surface Model) which consists of 2-D grids with elevation information on each grid. In this research, the inaccurate DSM is updated using both estimated robot pose and a local elevation map built by laser range data. In order to match the reference DSM with the local elevation map, ICP (Iterative Closest Points)-based scan matching technique with COAG (commonly observed from air and ground) features is used. Also, the robot pose is estimated by MCL (Monte Carlo localization). Experimental results show that the updated DSM yields better performance in localization compared to non-updated DSM. Error analysis of estimated paths from each map is presented with respect to the ground truth.
AB - Localization of a mobile robot is a very important task for autonomous navigation. However, with only an initially inaccurate map, a mobile robot cannot estimate its pose robustly because of the inconsistency between the real observations from the environment and the predicted observations on the inaccurate map. The main map used for outdoor environment is DSM (Digital Surface Model) which consists of 2-D grids with elevation information on each grid. In this research, the inaccurate DSM is updated using both estimated robot pose and a local elevation map built by laser range data. In order to match the reference DSM with the local elevation map, ICP (Iterative Closest Points)-based scan matching technique with COAG (commonly observed from air and ground) features is used. Also, the robot pose is estimated by MCL (Monte Carlo localization). Experimental results show that the updated DSM yields better performance in localization compared to non-updated DSM. Error analysis of estimated paths from each map is presented with respect to the ground truth.
UR - http://www.scopus.com/inward/record.url?scp=84863121840&partnerID=8YFLogxK
U2 - 10.1109/SII.2011.6147627
DO - 10.1109/SII.2011.6147627
M3 - Conference contribution
AN - SCOPUS:84863121840
SN - 9781457715235
T3 - 2011 IEEE/SICE International Symposium on System Integration, SII 2011
SP - 1245
EP - 1250
BT - 2011 IEEE/SICE International Symposium on System Integration, SII 2011
T2 - 2011 IEEE/SICE International Symposium on System Integration, SII 2011
Y2 - 20 December 2011 through 22 December 2011
ER -