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浙江大学学报(工学版)
浙江大学学报(工学版)  2024, Vol. 58 Issue (11): 2280-2289    DOI: 10.3785/j.issn.1008-973X.2024.11.009
计算机技术、控制工程     
基于改进卡尔曼滤波的轻量级激光惯性里程计
罗钒睿1(),刘振宇1,*(),任佳辉2,李笑宇1,程阳1
1. 沈阳工业大学 信息科学与工程学院，辽宁 沈阳 310058
2. 辽宁工业大学 软件学院，辽宁 锦州 310058
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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摘要：

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    
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
收稿日期: 2023-08-28 出版日期: 2024-10-23
CLC:  TP 393  
基金资助: 辽宁省应用基础研究计划资助项目（2023JH2/101300225）.
通讯作者: 刘振宇     E-mail: 2354971839@qq.com;liuzhenyu@sut.edu.cn
作者简介: 罗钒睿（1999—），男，硕士生，从事激光SLAM的研究. orcid.org/0009-0005-3257-5882. E-mail：2354971839@qq.com
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罗钒睿
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引用本文:

罗钒睿,刘振宇,任佳辉,李笑宇,程阳. 基于改进卡尔曼滤波的轻量级激光惯性里程计[J]. 浙江大学学报(工学版), 2024, 58(11): 2280-2289.

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.

链接本文:

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

图 1  LCG-LIO系统的总体框架图
图 2  双向降维曲率滤波的示意图
图 3  特征提取结果图
图 4  GPS约束和伪轨迹加权的示意图
图 5  KITTI运行结果图
图 6  KITTI运行轨迹图
图 7  KITTI运行局部细节轨迹
里程计算法$ {\partial _{\max}} $$ {\partial _{{\mathrm{mean}}}} $$ {\partial _{{\mathrm{median}}}} $$ {\partial _{{\mathrm{min}}}} $$ {\partial _{{\mathrm{rmse}}}} $
FAST-LIO236.13118.36918.2392.54120.417
LCG-LIO不添加GPS25.84810.4689.7122.54811.264
LCG-LIO23.7569.9398.5893.2099.162
表 1  KITTI运行的绝对位姿误差
图 8  自采数据集的运行结果图
里程计算法$ \Delta x $$ \Delta y $$ \Delta z $总体回环误差
LIO-SAM0.1200.2410.8800.920
LCG-LIO0.0400.0330.0030.052
表 2  自己采集的数据集运行回环误差
方法Npt1/mst2/mst/ms
FAST-LIO230156030.01230.012
LCG-LIO不添加GPS约束52125.7638.03213.795
LCG-LIO52125.7638.34314.106
表 3  各模块的平均时间消耗
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