The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 273–281, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-273-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 273–281, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-273-2022
 
30 May 2022
30 May 2022

GLOBALLY OPTIMAL POINT CLOUD REGISTRATION FOR ROBUST MOBILE MAPPING

D. Skuddis and N. Haala D. Skuddis and N. Haala
  • Institute for Photogrammetry, University of Stuttgart, Germany

Keywords: Point Cloud Registration, Scan Matching, Branch and Bound, Global Optimization, Mobile Mapping, LiDAR

Abstract. Point cloud registration algorithms have been studied for several decades. In the SLAM domain, dense local convergence based methods are typically used to register consecutive scans. Since these procedures are not globally optimal, it happens that they converge to a wrong local minimum. This can lead to gross errors during mapping and can make entire datasets unusable. We introduce a new branch and bound based point cloud registration method that is globally optimal. The method is able to reliably determine the global optimum within a given parameter search space. We show how this method can be used in a mapping system as a fallback function to correct gross errors. Using various public datasets, we demonstrate the capabilities of the method.