Loop-closure detection algorithm based on point cloud histogram and vehicle positioning method

被引：0

作者：

Li S.-T. ^{[1
]}

Li J.-L. ^{[1
,2
]}

Meng Q.-Y. ^{[1
,2
]}

Guo H.-Y. ^{[1
,2
]}

机构：

[1] College of Communication Engineering, Jilin University, Changchun

[2] State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2023年 / 53卷 / 08期

关键词：

control science and engineering; loop-closure detection; point cloud histogram; vehicle positioning;

D O I：

10.13229/j.cnki.jdxbgxb.20211090

中图分类号：

学科分类号：

摘要：

Aiming at the problem of inaccurate positioning of intelligent vehicles due to the loss of GPS signals on complex urban roads，a factor graph optimization model is established to perform data fusion on LiDAR （Light detection and ranging）and Inertial Measurement Unit （IMU），and the lidar-inertial odometry（LiDAR-inertial odometry，LIO）vehicle positioning method under the tightly-coupled framework is proposed，which can estimate vehicle state information in real time；a loop-closure detection algorithm based on point cloud histograms is proposed. The similarity of the point cloud determines whether the vehicle has reached the same position，and then combines the information from the last time that the vehicle passed the position to correct the current state of the vehicle，reducing the accumulation of positioning errors. The test results on the KITTI dataset show that the loop-closure detection module can effectively reduce the error accumulation of the LIO，and the LIO with the loop-closure detection module has excellent positioning accuracy. © 2023 Editorial Board of Jilin University. All rights reserved.

引用

页码：2395 / 2403

页数：8

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