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Review of mobile laser scanning target‐free registration methods for urban areas using improved error metrics
Author(s) -
Nguyen Hoang Long,
Belton David,
Helmholz Petra
Publication year - 2019
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1111/phor.12293
Subject(s) - point cloud , metric (unit) , computer science , laser scanning , artificial intelligence , matching (statistics) , computer vision , image registration , orientation (vector space) , point (geometry) , data mining , algorithm , laser , mathematics , statistics , image (mathematics) , engineering , optics , geometry , operations management , physics
Registration is one of the most important tasks in mobile laser scanning (MLS) point cloud processing. This paper firstly reviews existing target‐free matching techniques as well as methods to evaluate the quality of the registration. Next, a new error metric is introduced that takes into account the residuals of check planes as well as their orientation. Experiments using real datasets in combination with reference data were performed to evaluate the suitability of these metrics. The proposed error metric proved to be more suitable for evaluating the quality of point cloud registration than state‐of‐the‐art equivalents. The results also indicate that least squares plane fitting is the best technique for MLS point cloud registration.