The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 493–500, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-493-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 493–500, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-493-2016

  15 Jun 2016

15 Jun 2016

SPEEDING UP COARSE POINT CLOUD REGISTRATION BY THRESHOLD-INDEPENDENT BAYSAC MATCH SELECTION

Z. Kang1, R. Lindenbergh2, and S. Pu3 Z. Kang et al.
  • 1Department of Remote Sensing and Geo-Information Engineering, School of Land Science and Technology, China University of Geosciences, 29 Xueyuan Road, Haidian District, Beijing 100083, China
  • 2Chair Optical and Laser Remote Sensing, Dept. Geosciences and Remote Sensing, Fact. Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN, Delft, The Netherlands
  • 3Beijing Tovos Technology Co., Ltd, Unit 1602, TOP Building Tower B, Haidian Dajie No.3, Haidian District. Beijing, China

Keywords: BaySAC, Convergence evaluation, Hypothesis set, Inlier probability, Point cloud registration, RANSAC

Abstract. This paper presents an algorithm for the automatic registration of terrestrial point clouds by match selection using an efficiently conditional sampling method -- threshold-independent BaySAC (BAYes SAmpling Consensus) and employs the error metric of average point-to-surface residual to reduce the random measurement error and then approach the real registration error. BaySAC and other basic sampling algorithms usually need to artificially determine a threshold by which inlier points are identified, which leads to a threshold-dependent verification process. Therefore, we applied the LMedS method to construct the cost function that is used to determine the optimum model to reduce the influence of human factors and improve the robustness of the model estimate. Point-to-point and point-to-surface error metrics are most commonly used. However, point-to-point error in general consists of at least two components, random measurement error and systematic error as a result of a remaining error in the found rigid body transformation. Thus we employ the measure of the average point-to-surface residual to evaluate the registration accuracy. The proposed approaches, together with a traditional RANSAC approach, are tested on four data sets acquired by three different scanners in terms of their computational efficiency and quality of the final registration. The registration results show the st.dev of the average point-to-surface residuals is reduced from 1.4 cm (plain RANSAC) to 0.5 cm (threshold-independent BaySAC). The results also show that, compared to the performance of RANSAC, our BaySAC strategies lead to less iterations and cheaper computational cost when the hypothesis set is contaminated with more outliers.