A point cloud registration algorithm based on normal vector and particle swarm optimization

Xu Zhan, Yong Cai, Heng Li, Yangmin Li, Ping He

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Based on normal vector and particle swarm optimization (NVP), a point cloud registration algorithm is proposed by searching the corresponding points. It provides a new method for point cloud registration using feature point registration. First, in order to find the nearest eight neighbor nodes, the k-d tree is employed to build the relationship between points. Then, the normal vector and the distance between the point and the center gravity of eight neighbor points can be calculated. Second, the particle swarm optimization is used to search the corresponding points. There are two conditions to terminate the search in particle swarm optimization: one is that the normal vector of node in the original point cloud is the most similar to that in the target point cloud, and the other is that the distance between the point and the center gravity of eight neighbor points of node is the most similar to that in the target point cloud. Third, after obtaining the corresponding points, they are tested by random sample consensus in order to obtain the right corresponding points. Fourth, the right corresponding points are registered by the quaternion method. The experiments demonstrate that this algorithm is effective. Even in the case of point cloud data lost, it also has high registration accuracy.

Original languageEnglish
JournalMeasurement and Control (United Kingdom)
DOIs
Publication statusAccepted/In press - 1 Jan 2019

Keywords

  • K-d tree
  • normal vector
  • quaternion method

ASJC Scopus subject areas

  • Instrumentation
  • Control and Optimization
  • Applied Mathematics

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