Tightly-coupled SLAM algorithm integrating LiDAR/IMU/vehicle kinematic constraints

被引：0
|
作者：
Yang, Xiujian [1 ]
Yan, Shaoxiang [1 ]
Huang, Jialong [1 ]
机构：
[1] Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming,650500, China
来源：
Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology | 2024年 / 32卷 / 06期
关键词：
Targeting the positioning requirements of autonomous vehicles in scenarios with insufficient global navigation satellite system (GNSS) signals; a tightly-coupled simultaneous localization and mapping (SLAM) algorithm that integrates LiDAR; inertial measurement unit (IMU) and vehicle kinematic constraints is proposed. First; vehicle kinematic constraints are established with the angular rate of IMU; rear wheel speed; and front wheel angle. By decoupling the displacement and orientation information of the vehicle motion; the constraints for displacement and orientation are formulated separately to enhance the accuracy of optimization. Then; adaptive weights are introduced based on the number of feature points and the steering angle to dynamically adjust the weight of the vehicle kinematic constraints in real time. Finally; an odometer is constructed based on the angular rate of IMU and rear wheel speed; providing precise initial values for the back-end tightly-coupled optimization and effectively preventing falling into local optima. Results from tests conducted in various road scenarios demonstrate compared with the algorithms of LeGO_LOAM and LIO_SAM; the average planar positioning accuracy of the proposed algorithm is improved by 32% and 29% respectively; which provides a short-term high-precision positioning solution for autonomous vehicles in situations where GNSS signals are insufficient. © 2024 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved;
D O I：
10.13695/j.cnki.12-1222/o3.2024.06.003
中图分类号：
学科分类号：
摘要：
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页码：547 / 554
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