TY - GEN
T1 - Strong Tracking Filter Simultaneous Localization and Mapping algorithm
AU - Li, Huiping
AU - Xu, Demin
AU - Yao, Yao
AU - Zhang, Fubin
PY - 2008
Y1 - 2008
N2 - Simultaneous Localization and Mapping (SLAM) is a central and complex problem in robot research community. In SLAM, Extended Kalman Filter (EKF) implementation is widely used to localize the robot and build the environment map incrementally. In this paper, we propose a Strong Tracking Filter (STF) SLAM algorithm. This algorithm applies STF to deal with the non-linear estimated problem in SLAM instead of EKF. It can make the performance of the nonlinear filter approximate to that of optimal linear Kalman Filter (KF), so it can construct highly accurate maps and locate the robot more accurately than EKF SLAM. Simulation experiments illustrate the superior performance of our approach compared to EKF SLAM algorithm.
AB - Simultaneous Localization and Mapping (SLAM) is a central and complex problem in robot research community. In SLAM, Extended Kalman Filter (EKF) implementation is widely used to localize the robot and build the environment map incrementally. In this paper, we propose a Strong Tracking Filter (STF) SLAM algorithm. This algorithm applies STF to deal with the non-linear estimated problem in SLAM instead of EKF. It can make the performance of the nonlinear filter approximate to that of optimal linear Kalman Filter (KF), so it can construct highly accurate maps and locate the robot more accurately than EKF SLAM. Simulation experiments illustrate the superior performance of our approach compared to EKF SLAM algorithm.
UR - https://www.scopus.com/pages/publications/61849167975
U2 - 10.1109/CSSE.2008.487
DO - 10.1109/CSSE.2008.487
M3 - 会议稿件
AN - SCOPUS:61849167975
SN - 9780769533360
T3 - Proceedings - International Conference on Computer Science and Software Engineering, CSSE 2008
SP - 1085
EP - 1088
BT - Proceedings - International Conference on Computer Science and Software Engineering, CSSE 2008
T2 - International Conference on Computer Science and Software Engineering, CSSE 2008
Y2 - 12 December 2008 through 14 December 2008
ER -