Open AccessFast Closed-Loop SLAM based on the fusion of IMU and Lidar
Author(s) -
Luo Hengjie,
Hong Bao,
Cheng Xu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1914/1/012019
Subject(s) - inertial measurement unit , lidar , simultaneous localization and mapping , computer science , computer vision , artificial intelligence , sensor fusion , fusion , matching (statistics) , mathematics , remote sensing , mobile robot , geography , robot , statistics , linguistics , philosophy
In the large-scale mapping of Lidar SLAM, there are problems of slow closed-loop detection matching speed and false detection. To solve this problem, this paper proposed a method based on the fusion of IMU and Lidar. Use IMU geomagnetic data for geomagnetic matching and filter the pose nodes in the map to reduce the search space during closed-loop detection. At the same time, due to the reduction of the search space, it can effectively reduce the high local similarity in Lidar SLAM false detection caused. This experiment verified the method by collecting Lidar point cloud data and IMU three-axis geomagnetic data in a real environment, the closed-loop detection speed and map average absolute error of frame-by-frame method, branch-and-bound method, DTW fusion algorithm and Fast-DTW fusion algorithm were verified and compared. Experimental results shown that, compared with DTW fusion algorithm, the proposed algorithm improved the closed-loop detection speed by 17%, reduced the average absolute error of translation by 16%, and reduced the average absolute error of rotation by 10%.