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

Chinese Optics Letters, Volume. 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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Figures & Tables(7)

Fig. 1. Proposed object tracking method of point cloud based on JGLF.

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Fig. 2. Flow chart of the proposed object tracking method.

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Fig. 3. Comparison of the object recognition rate at different distances between LIDAR and the object.

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Fig. 4. Results of the PF point cloud tracking algorithm based on JGLF in frame $n$ : (a) $n = 1$ , (b) $n = 61$ , (c) $n = 121$ , (d) $n = 181$ , and (e) $n = 241$ . (f) Comparison between particles in frame 186.

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Fig. 5. Comparison of the object tracking effect between the two algorithms.

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Table 1. Object Recognition Ability Comparison of Six Descriptors
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Table 1. Object Recognition Ability Comparison of Six Descriptors
Object Recognition Rate (%) Average Running Time (ms)
Mean Standard Deviation
FPFH 62.37 13.17 5
VFH 64.34 11.25 3.6
CVFH 68.91 7.79 4.5
GRSD 39.85 16.28 31
ESF 87.59 16.71 39
JGLF 72.09 10.81 6

Table 2. Comparison of Tracking Results of Five Algorithms

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Table 2. Comparison of Tracking Results of Five Algorithms

	Tracking Accuracy (%)	R of Single Frame (%)		Average Running Time (ms)	CPU Utilized Percent (%)
	Tracking Accuracy (%)	Mean	Standard Deviation	Average Running Time (ms)	CPU Utilized Percent (%)
Basic algorithm	84.87	72.81	17.75	12.44	5
Algorithm based on FPFH	89.04	80.67	12.47	12.71	7
Algorithm based on VFH	90.76	82.21	14.59	12.68	7
Algorithm based on CVFH	91.25	80.33	9.07	12.82	8
Proposed algorithm	98.82	88.01	11.96	12.96	8

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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

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