TY - GEN
T1 - Research on localization and mapping for lunar rover based on RBPF-SLAM
AU - Yan, Ma
AU - Hehua, Ju
AU - Pingyuan, Cui
PY - 2009
Y1 - 2009
N2 - The capability of autonomous navigation is very important for lunar rover exploring in an unknown environment. In this paper, a method of simultaneous localization and mapping based on Rao-Blackwellized particle filters (RBPF-SLAM) is adopted to improve the precision of inertial positioning for the rover and to build a 2D grid map for the environment. Lunar rover's motion model is built by combining Strapdown Inertial Navigation System (SINS) with a kind of odometry model, and the observation model of LiDAR is built using a likelihood field (LF) approach. Then the traditional RBPF-SLAM algorithm is improved: First, the most recent observation and the global map built before are considered in the proposal distribution; second, a grid-based incremental mapping method is presented. The results of simulation experiment show that the precision of localization by SINS is improved significantly using this method of RBPF-SLAM and an accurate and consistent 2D grid map is built successfully.
AB - The capability of autonomous navigation is very important for lunar rover exploring in an unknown environment. In this paper, a method of simultaneous localization and mapping based on Rao-Blackwellized particle filters (RBPF-SLAM) is adopted to improve the precision of inertial positioning for the rover and to build a 2D grid map for the environment. Lunar rover's motion model is built by combining Strapdown Inertial Navigation System (SINS) with a kind of odometry model, and the observation model of LiDAR is built using a likelihood field (LF) approach. Then the traditional RBPF-SLAM algorithm is improved: First, the most recent observation and the global map built before are considered in the proposal distribution; second, a grid-based incremental mapping method is presented. The results of simulation experiment show that the precision of localization by SINS is improved significantly using this method of RBPF-SLAM and an accurate and consistent 2D grid map is built successfully.
KW - Incremental mapping
KW - Likelihood field
KW - Lunar rover
KW - Rao-Blackwellized particle filter
KW - Simultaneous localization and mapping
UR - http://www.scopus.com/inward/record.url?scp=73649122414&partnerID=8YFLogxK
U2 - 10.1109/IHMSC.2009.200
DO - 10.1109/IHMSC.2009.200
M3 - Conference contribution
AN - SCOPUS:73649122414
SN - 9780769537528
T3 - 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009
SP - 306
EP - 311
BT - 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009
T2 - 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009
Y2 - 26 August 2009 through 27 August 2009
ER -