TY - GEN
T1 - Multi-sensor Fusion Based Indoor Mobile Robot Localization
AU - Liu, Rui
AU - Xu, Jun
AU - Lou, Yunjiang
AU - Chen, Haoyao
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, a multi-sensor fusion framework is proposed to solve the localization problem of mobile robot in indoor environments. To improve the localization accuracy, two kinds of fusion algorithms, namely extended Kalman filter(EKF) and Monte Carlo localization(MCL), are used and the motion model as well as the measurement model are selected according to the complexity of the environment, which is quantified by the minimum distance between the robot and the obstacles. In EKF fusion, the motion model is obtained by wheel odometer, and the measurement model is the combination of inertial measurement unit(IMU) and ultra-wideband(UWB) sensor. While in MCL fusion, the motion model switches between odometry and the output of EKF, and the measurement model switches between lidar and the combination of IMU and UWB sensor. Experimental results show that the localization effect of multi-sensor fusion is better than that of a single sensor. The improved MCL algorithm proposed in this paper is superior to the traditional Monte Carlo localization algorithm in both localization accuracy and convergence speed.
AB - In this paper, a multi-sensor fusion framework is proposed to solve the localization problem of mobile robot in indoor environments. To improve the localization accuracy, two kinds of fusion algorithms, namely extended Kalman filter(EKF) and Monte Carlo localization(MCL), are used and the motion model as well as the measurement model are selected according to the complexity of the environment, which is quantified by the minimum distance between the robot and the obstacles. In EKF fusion, the motion model is obtained by wheel odometer, and the measurement model is the combination of inertial measurement unit(IMU) and ultra-wideband(UWB) sensor. While in MCL fusion, the motion model switches between odometry and the output of EKF, and the measurement model switches between lidar and the combination of IMU and UWB sensor. Experimental results show that the localization effect of multi-sensor fusion is better than that of a single sensor. The improved MCL algorithm proposed in this paper is superior to the traditional Monte Carlo localization algorithm in both localization accuracy and convergence speed.
UR - https://www.scopus.com/pages/publications/85141669164
U2 - 10.1109/CASE49997.2022.9926435
DO - 10.1109/CASE49997.2022.9926435
M3 - 会议稿件
AN - SCOPUS:85141669164
T3 - IEEE International Conference on Automation Science and Engineering
SP - 22
EP - 27
BT - 2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022
PB - IEEE Computer Society
T2 - 18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Y2 - 20 August 2022 through 24 August 2022
ER -