Plane-based Accurate Registration of Real-world Point Clouds - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Plane-based Accurate Registration of Real-world Point Clouds

Abstract

Traditional 3D point clouds registration algorithms, based on Iterative Closest Point (ICP), rely on point matching of large point clouds. In well-structured environments, such as buildings, planes can be segmented and used for registration, similarly to the classical point-based ICP approach. Using planes tremendously reduces the number of inputs. In this article, an efficient plane-based registration algorithm is presented. The optimal transformation is estimated through a two-step approach, successively performing robust plane-toplane minimization and non-linear robust point-to-plane registration. Experiments on the Autonomous Systems Lab (ASL) benchmark 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 robust in large motion scenarios and more accurate than other state-of-the-art algorithms. Moreover, a new challenging dataset, LOOP'IN, is provided. It is composed of two loops in real-world indoor scenes, with a large number of scans captured with a 3D LiDAR. Tests led on this dataset show that the algorithm is able to register long sequences, to close loops and to build an incremental map of the explored environment.
Fichier principal
Vignette du fichier
SMC21_0145_FI.pdf (616.76 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03329646 , version 1 (31-08-2021)

Identifiers

Cite

Ketty Favre, Muriel Pressigout, Eric Marchand, Luce Morin. Plane-based Accurate Registration of Real-world Point Clouds. SMC 2021 - IEEE International Conference on Systems, Man, and Cybernetics, Oct 2021, Melbourne / Virtual, Australia. pp.2018-2023, ⟨10.1109/SMC52423.2021.9658727⟩. ⟨hal-03329646⟩
88 View
255 Download

Altmetric

Share

Gmail Facebook X LinkedIn More