TY - GEN
T1 - Unscented SLAM with conditional iterations
AU - Zhu, Jihua
AU - Zheng, Nanning
AU - Yuan, Zejian
AU - Zhang, Qiang
AU - Zhang, Xuetao
PY - 2009
Y1 - 2009
N2 - As reported, the extended Kaiman Filter based Simultaneous Localization and Mapping (SLAM) algorithm has two serious drawbacks, namely the linear approximation of nonlinear functions and the calculation of Jacobian matrices. These can introduce estimation error and induce a great ambiguity for data association. For overcoming these drawbacks, this paper presents an improved SLAM solution, based on the Unscented Kaiman Filter (UKF) with conditional iterations (UiSLAM). Since the UKF can improve the performance of filters, it can be used to overcome the drawbacks of the previous frameworks. When the loop is closed, the condition to perform iterated update is satisfied. Then the iterative update procedure employed in the iterated extended Kaiman Filter (IEKF) is implemented. This approach combines the virtues of IEKF and UKF for solving the SLAM problems and improves accuracy of the state estimation. Both the simulation and experimental results are proposed to illustrate the superiority of the UiSLAM algorithm over previous approaches.
AB - As reported, the extended Kaiman Filter based Simultaneous Localization and Mapping (SLAM) algorithm has two serious drawbacks, namely the linear approximation of nonlinear functions and the calculation of Jacobian matrices. These can introduce estimation error and induce a great ambiguity for data association. For overcoming these drawbacks, this paper presents an improved SLAM solution, based on the Unscented Kaiman Filter (UKF) with conditional iterations (UiSLAM). Since the UKF can improve the performance of filters, it can be used to overcome the drawbacks of the previous frameworks. When the loop is closed, the condition to perform iterated update is satisfied. Then the iterative update procedure employed in the iterated extended Kaiman Filter (IEKF) is implemented. This approach combines the virtues of IEKF and UKF for solving the SLAM problems and improves accuracy of the state estimation. Both the simulation and experimental results are proposed to illustrate the superiority of the UiSLAM algorithm over previous approaches.
UR - https://www.scopus.com/pages/publications/70449585308
U2 - 10.1109/IVS.2009.5164266
DO - 10.1109/IVS.2009.5164266
M3 - 会议稿件
AN - SCOPUS:70449585308
SN - 9781424435043
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 134
EP - 139
BT - 2009 IEEE Intelligent Vehicles Symposium
T2 - 2009 IEEE Intelligent Vehicles Symposium
Y2 - 3 June 2009 through 5 June 2009
ER -