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：

引用

页码：547 / 554

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