Lightweight LiDAR-IMU odometry based on improved Kalman filter

doi:10.3785/j.issn.1008-973X.2024.11.009

Journal of ZheJiang University (Engineering Science)

2024, Vol. 58

Issue (11): 2280-2289 DOI: 10.3785/j.issn.1008-973X.2024.11.009

Lightweight LiDAR-IMU odometry based on improved Kalman filter

Fanrui LUO1(

),Zhenyu LIU1,*(

),Jiahui REN2,Xiaoyu LI1,Yang CHENG1

1. School of Information Science and Engineering, Shenyang University of Technology, Shenyang 310058, China
2. School of Software, Liaoning University of Technology, Jinzhou 310058, China

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Abstract

LCG-LIO was proposed based on the FAST-LIO2 aiming at the problem of real-time localization and running poor stability of mobile robot. LCG-LIO exhibited lower computational complexity and increased localization accuracy compared with FAST-LIO2. The LCG-LIO frontend incorporated a method for extracting and segmenting high-quality plane and ground points by using the proposed bidirectional dimensionality reduction curvature filter in contrast to FAST-LIO2. LCG-LIO balances the number of plane and ground points through the pseudo occupancy of point clouds. Improvements were made to the observation error equations and the construction of its Jacobian matrix for the Kalman filter in the backend optimization. The GPS constraint was incorporated to the observation error equation by pseudo-trajectory weighting method, and cumulative odometry drift was corrected. Experimental validation was performed by using the KITTI dataset and self-collected datasets. Results showed that the accuracy and efficiency of the proposed method were improved by 55.13% and 53.01% compared with FAST-LIO2. The proposed method for integrating GPS has higher feasibility than the factor graph optimization in LIO-SAM.

Key words： LiDAR-IMU odometry feature extraction improved Kalman filtering GPS constraint

Received: 28 August 2023 Published: 23 October 2024

CLC:

TP 393

Fund: 辽宁省应用基础研究计划资助项目（2023JH2/101300225）.

Corresponding Authors: Zhenyu LIU E-mail: 2354971839@qq.com;liuzhenyu@sut.edu.cn

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Cite this article:

Fanrui LUO,Zhenyu LIU,Jiahui REN,Xiaoyu LI,Yang CHENG. Lightweight LiDAR-IMU odometry based on improved Kalman filter. Journal of ZheJiang University (Engineering Science), 2024, 58(11): 2280-2289.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.11.009 OR https://www.zjujournals.com/eng/Y2024/V58/I11/2280

基于改进卡尔曼滤波的轻量级激光惯性里程计

针对移动机器人长期的实时定位和运行的稳定性较差的问题，在激光里程计FAST-LIO2的基础上，提出LCG-LIO，其较FAST-LIO2具有更少的计算量和更高的定位精度. 相较于FAST -LIO2，LCG-LIO前端加入了提出的双向降维曲率滤波的高质量平面与地面点云提取和分割的方法，通过点云伪占用的方法，平衡了平面和地面点的数量. 在后端优化中，改进了卡尔曼滤波的观测误差方程和观测误差雅可比矩阵的构建方法，在观测误差方程中加入了GPS 约束，通过伪轨迹加权的方法，纠正了里程计的累计漂移. 通过KITTI数据集和自己采集的数据集，对提出的方法进行实验. 结果表明，提出方法的精度和效率较FAST-LIO2提高了55.13%和53.01%，提出的GPS信息融合方法较LIO-SAM中的因子图优化方法具有更高的可行性.

关键词： 激光惯性里程计, 特征提取, 改进的卡尔曼滤波, GPS约束

Fig.1 Overall framework of LCG-LIO system

Fig.2 Diagram of bidirectional dimensionality reduction curvature filter

Fig.3 Feature extraction results

Fig.4 Schematic diagram of GPS constraints and pseudo trajectory weighting

Fig.5 KITTI operation result

Fig.6 KITTI operation trajectory

Fig.7 KITTI operation partial detail trajectory

Tab.1 Absolute pose error of KITTI operation

Fig.8 Operation result of self-collected dataset

Tab.2 Self collected dataset running loopback error

Tab.3 Average time consumption of each module


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