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A Plane-based Approach for Indoor Point Clouds Registration

Abstract : Iterative Closest Point (ICP) is one of the mostly used algorithms for 3D point clouds registration. This classical approach can be impacted by the large number of points contained in a point cloud. Planar structures, which are less numerous than points, can be used in well-structured man-made environment. In this paper we propose a registration method inspired by the ICP algorithm in a plane-based registration approach for indoor environments. This method is based solely on data acquired with a LiDAR sensor. A new metric based on plane characteristics is introduced to find the best plane correspondences. The optimal transformation is estimated through a two-step minimization approach, successively performing robust plane-to-plane minimization and non-linear robust point-to-plane registration. Experiments on the Autonomous Systems Lab (ASL) dataset show that the proposed method enables to successfully register 100% of the scans from the three indoor sequences. Experiments also show that the proposed method is more robust in large motion scenarios than other state-of-the-art algorithms.
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Contributor : Ketty Favre <>
Submitted on : Wednesday, January 13, 2021 - 2:36:59 PM
Last modification on : Monday, January 18, 2021 - 9:02:57 AM


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  • HAL Id : hal-03108891, version 1


Ketty Favre, Muriel Pressigout, Eric Marchand, Luce Morin. A Plane-based Approach for Indoor Point Clouds Registration. ICPR 2020 - 25th International Conference on Pattern Recognition, Jan 2021, Milan (Virtual), Italy. ⟨hal-03108891⟩



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