Object tracking method based on joint global and local feature descriptor of 3D LIDAR point cloud

被引：8
作者：
Qian, Qishu [1 ,2 ]
Hu, Yihua [1 ,2 ]
Zhao, Nanxiang [1 ,2 ]
Li, Minle [1 ,2 ]
Shao, Fucai [3 ]
Zhang, Xinyuan [1 ,2 ]
机构：
[1] Natl Univ Def Technol, State Key Lab Pulsed Power Laser Technol, Hefei 230037, Peoples R China
[2] Natl Univ Def Technol, Anhui Prov Key Lab Elect Restrict, Hefei 230037, Peoples R China
[3] Cent Mil Commiss Beijing, Mil Representat Bur, Minist Equipment Dev, Beijing 100191, Peoples R China
来源：
CHINESE OPTICS LETTERS | 2020年 / 18卷 / 06期
基金：
中国国家自然科学基金;
关键词：
object tracking; LIDAR; global and local feature descriptor; point cloud; PARTICLE FILTER; POSE;
D O I：
10.3788/COL202018.061001
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
To fully describe the structure information of the point cloud when the LIDAR-object distance is long, a joint global and local feature (JGLF) descriptor is constructed. Compared with five typical descriptors, the object recognition rate of JGLF is higher when the LIDAR-object distances change. Under the situation that airborne LIDAR is getting close to the object, the particle filtering (PF) algorithm is used as the tracking frame. Particle weight is updated by comparing the difference between JGLFs to track the object. It is verified that the proposed algorithm performs 13.95% more accurately and stably than the basic PF algorithm.
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页数：6
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