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

Qishu Qian; Yihua Hu; Nanxiang Zhao; Minle Li; Fucai Shao; Xinyuan Zhang

doi:10.3788/COL202018.061001

Journals >Chinese Optics Letters >Volume 18 >Issue 6 >Page 061001 > Article

Chinese Optics Letters
Vol. 18, Issue 6, 061001 (2020)

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

Qishu Qian^1、2, Yihua Hu^1、2、*, Nanxiang Zhao^1、2, Minle Li^1、2, Fucai Shao³, and Xinyuan Zhang^1、2

Author Affiliations

¹State Key Laboratory of Pulsed Power Laser Technology, National University of Defense Technology, Hefei 230037, China

²Anhui Provincial Key Laboratory of Electronic Restriction, National University of Defense Technology, Hefei 230037, China

³The Military Representative Bureau of the Ministry of Equipment Development, Central Military Commission in Beijing, Beijing 100191, China

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DOI: 10.3788/COL202018.061001 Cite this Article Set citation alerts

Qishu Qian, Yihua Hu, Nanxiang Zhao, Minle Li, Fucai Shao, Xinyuan Zhang. Object tracking method based on joint global and local feature descriptor of 3D LIDAR point cloud[J]. Chinese Optics Letters, 2020, 18(6): 061001 Copy Citation Text

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Abstract

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.

Keywords

global and local feature descriptor LIDAR object tracking point cloud

sum = \sum FPFH (P_{q}) + \sum VFH (Q^{q}) .

(1)