Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (8): 2395-2403.doi: 10.13229/j.cnki.jdxbgxb.20211090

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

Shou-tao LI1(),Jia-lin LI1,2,Qing-yu MENG1,2,Hong-yan GUO1,2()   

  1. 1.College of Communication Engineering，Jilin University，Changchun 130022，China
    2.State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
  • Received:2021-10-23 Online:2023-08-01 Published:2023-08-21
  • Contact: Hong-yan GUO E-mail:list@jlu.edu.cn;guohy11@jlu.edu.cn

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Abstract:

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.

Key words: control science and engineering, point cloud histogram, loop-closure detection, vehicle positioning

CLC Number: 

  • TP391.4

Fig.1

System Block Diagram"

Fig.2

Schematic of motion compensation"

Fig.3

IMU pre-integration principle"

Fig.4

Sliding window operation strategy"

Fig.5

Classification of cells"

Fig.6

05 Sequence comparison of trajectory of each system"

Fig.7

05 Sequence LIO-loop trajectory error"

Fig.8

Point cloud histogram of vehicle passing point A for the first time"

Fig.9

Point cloud histogram of vehicle passing point A for the second time"

Fig.10

Absolute position error statistics of 05 sequence LIO-loop trajectory"

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