Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 1-7.doi: 10.6040/j.issn.1672-3961.0.2018.244

• Machine Learning & Data Mining •     Next Articles

Fast 4-points congruent sets for coarse registration of 3D point cloud

Shiguang LIU1,2(),Hairong WANG1,Jin LIU1   

  1. 1. Division of Intelligence and Computing, Tianjin University, Tianjin 300350, Tianjin, China
    2. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin 300350, Tianjin, China
  • Received:2018-06-07 Online:2019-04-20 Published:2019-04-19
  • Supported by:
    国家自然科学基金项目(61672375);国家自然科学基金项目(61170118);天津市自然科学基金项目(14JCQNJC00100)

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Abstract:

In order to solve the problem that the 4-points congruent sets (4 PCS) method suffered from low computational efficiency and high registration errors when the overlap rate of two pieces of input point clouds was low, fast 4-points congruent sets (F-4PCS) was put forward. A new method for selecting four-point basis was presented. The source point cloud and target point cloud were given, their boundaries were separately extracted and extended as the boundary feature bands, and then a consistent four-point basis set was chosen from the boundary feature bands. This method could avoid some unnecessary iterations. By limiting the characteristics of the four-point basis, the invalid four-point basis was removed, it could reduce the verification time of the algorithm and improve the computational efficiency. Experiments results carried out on the relevant data sets showed that the F-4PCS method was more efficient than conventional 4PCS method in the case of low overlap rate of input point clouds and the registration success rate was higher than state-of-the-arts.

Key words: point cloud, registration, feature, 4-point basis, 4PCS, F-4PCS

CLC Number: 

  • TP391.41

Fig.1

The 4-point basis and its representation"

Fig.2

Illustration of boundary extraction process"

Fig.3

Illustration of boundary extension to boundary feature belt"

Fig.4

Illustration of PFH feature calculation]"

Fig.5

The point cloud model of original input"

Fig.6

The point cloud registration results of the algorithm"

Fig.7

The point cloud registration results of Super-4PCS"

Fig.8

Comparison of running time among K-4PCS, Super-4PCS and the algorithm in this research"

Fig.9

Comparison of SR and RMSE between the paper algorithm and 4PCS"

Fig.10

The point cloud registration results of different noise points"

Fig.11

Registration results under noise interference"

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