Multi-frame Fusion Method for Point Cloud of LiDAR Based on IMU

Authors

Yanguo Zhang, Beijing Information Science Technology University Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing 100192, China;
Li Qing, Beijing Information Science Technology University Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing 100192, China;

Abstract

Abstract: Aiming at the problem that in the process of using 16-line laser radar to realize environment perception, the point cloud data is sparse, which leads to the difficulty of target detection and tracking, a new method of LiDAR point cloud fusion based on inertial measurement unit (IMU) is proposed. The method establishes a multi-frame LiDAR point cloud data fusion model, which can effectively use historical point cloud data and detection results to obtain more environmental information, and improve the detection accuracy and tracking ability of target objects. 16-line laser radar and the self-developed IMU sensor are used to conduct the tests. The results demonstrate that the proposed method can achieve the multi-frame fusion of the laser radar point cloud, and the detection and tracking ability of the laser radar can be further improved. And more advanced environment awareness is achieved with lower hardware costs, which shows that the method has practical application value for the study of driverless technology.

Recommended Citation

Zhang, Yanguo and Qing, Li (2019) "Multi-frame Fusion Method for Point Cloud of LiDAR Based on IMU," Journal of System Simulation: Vol. 30: Iss. 11, Article 34.
DOI: 10.16182/j.issn1004731x.joss.201811034
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss11/34

First Page

4334

Revised Date

2018-06-30

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201811034

Last Page

4339

CLC

TP391.9

Recommended Citation

Zhang Yanguo, Li Qing. Multi-frame Fusion Method for Point Cloud of LiDAR Based on IMU[J]. Journal of System Simulation, 2018, 30(11): 4334-4339.

DOI

10.16182/j.issn1004731x.joss.201811034