Research Article
LiDAR/DR-Integrated Mobile Robot Localization Employing IMM-EKF/PF Filtering
@INPROCEEDINGS{10.1007/978-3-030-51103-6_26, author={Ning Feng and Yong Zhang and Yuan Xu and Shuhui Bi and Tongqian Liu}, title={LiDAR/DR-Integrated Mobile Robot Localization Employing IMM-EKF/PF Filtering}, proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2020}, month={7}, keywords={Indoor robot location Kalman filter Particle filtering Interacting multiple model}, doi={10.1007/978-3-030-51103-6_26} }- Ning Feng
Yong Zhang
Yuan Xu
Shuhui Bi
Tongqian Liu
Year: 2020
LiDAR/DR-Integrated Mobile Robot Localization Employing IMM-EKF/PF Filtering
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-51103-6_26
Abstract
In order to solve the problems that indoor mobile robots have parking during the traveling process and the Extended Kalman filter (EKF) receives too much influence on parameter selection, this paper proposes an Interacting Multiple Model (IMM)-EKF/Particle Filtering (PF) adaptive algorithm for the tightly inertial navigation system (INS)/Light Detection And Ranging (LiDAR) integrated navigation. The EKF and PF calculate the position of the robot respectively, then the smallerMahalanobisdistance-based filter’s output is selected as the initial value of the next iteration, which improves the accuracy of the positioning for the robot. Based on that, the two motion equations of the static and normal motion models are dsigned at the same time. AMarkovchain for converting the two state of the model, and the weighting filtering result of the filtered is used to provide distance estimates. The real experimental results show that the IMM-EKF/PF adaptive algorithm improves the positioning accuracy of mobile robots in the presence of parking.
