Lidar Target Point Cloud Alignment Based on Improved Neighborhood Curvature with Iteration Closest Point Algorithm

Yanhong Li; Jianguo Yan; Xiaoyan Wang

doi:10.3788/LOP212521

Laser & Optoelectronics Progress, Volume. 60, Issue 2, 0228008(2023)

Lidar Target Point Cloud Alignment Based on Improved Neighborhood Curvature with Iteration Closest Point Algorithm

Yanhong Li^1,2、*, Jianguo Yan², and Xiaoyan Wang³

Author Affiliations

¹School of Physics & Electronic Engineering, Xianyang Normal University, Xianyang 712000, Shaanxi, China

²School of Automation, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China

³School of Electrical and Mechanical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China

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Figures & Tables(6)

Fig. 1. Registration results of Bunny in different algorithms. (a) Bunny origin cloud; (b) PCA coarse registration; (c) NDT algorithm; (d) ICP algorithm; (e) algorithm in reference [12]; (f) proposed improved ICP algorithm

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Fig. 2. Registration results of Airplane in different algorithms. (a) Airplane origin cloud; (b) PCA coarse registration; (c) NDT algorithm; (d) ICP algorithm; (e) algorithm in reference [12]; (f) proposed improved ICP algorithm

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Fig. 3. Registration results of Land in different algorithms. (a) Land origin cloud; (b) PCA coarse registration; (c) NDT algorithm; (d) ICP algorithm; (e) algorithm in reference [12]; (f) proposed improved ICP algorithm

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Table 1. Point cloud registration parameter setting
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Table 1. Point cloud registration parameter setting
Parameter $ε_{0}$ $τ$ $e_{0}$
Value 0.025 50 10^-8

Table 2. Registration efficiency of Bunny and Airplane for different methods

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Table 2. Registration efficiency of Bunny and Airplane for different methods

Method	Bunny		Airplane
Method	Ave-matching error /（10^-6 m）	Time-consuming /s	Ave-matching error /（10^-6 m）	Time-consuming /s
NDT	16.26	89.6	43.92	180.5
ICP	6.82	65.5	20.85	110.6
Reference ［12］	5.13	32.2	14.38	69.2
Proposed improved ICP	4.88	20.1	13.61	58.4

Table 3. Registration efficiency of Land for different methods
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Table 3. Registration efficiency of Land for different methods
Method Ave-matching error /（10^-2 m） Time-consuming /s
NDT 5.98 327.9
ICP 2.65 193.5
Reference ［12］ 2.53 120.1
Proposed improved ICP 2.38 92.6

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Yanhong Li, Jianguo Yan, Xiaoyan Wang. Lidar Target Point Cloud Alignment Based on Improved Neighborhood Curvature with Iteration Closest Point Algorithm[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0228008

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