A Fast and Accurate Planar-Feature-Based Global Scan Registration Method

Zong, Wenpeng; Li, Minglei; Zhou, Yue; Wang, Li; Xiang, Fengzhuo; Li, Guangyun

doi:10.1109/jsen.2019.2940478

Cited by 15 publications

(16 citation statements)

References 31 publications

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“…The registration process can be classified into two major categories: point-based and feature-based approaches. Point-based approaches use corresponding point pairs in both clouds and do not require complex processing algorithms [34,35]. Random sample consensus (RANSAC), proposed by Bolles et al [36], is extensively used to identify corresponding points.…”

Section: Related Workmentioning

confidence: 99%

Plane-Based Robust Registration of a Building Scan with Its BIM

2022

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The registration of as-built and as-planned building models is a pre-requisite in automated construction progress monitoring. Due to the numerous challenges associated with the registration process, it is still performed manually. This research study proposes an automated registration method that aligns the as-built point cloud of a building to its as-planned model using its planar features. The proposed method extracts and processes all the plane segments from both the as-built and the as-planned models, then—for both models—groups parallel plane segments into clusters and subsequently determines the directions of these clusters to eventually determine a range of possible rotation matrices. These rotation matrices are then evaluated through a computational framework based on a postulation concerning the matching of plane segments from both models. This framework measures the correspondence between the plane segments through a matching cost algorithm, thus identifying matching plane segments, which ultimately leads to the determination of the transformation parameters to correctly register the as-built point cloud to its as-planned model. The proposed method was validated by applying it to a range of different datasets. The results proved the robustness of the method both in terms of accuracy and efficiency. In addition, the method also proved its correct support for the registration of buildings under construction, which are inherently incomplete, bringing research a step closer to practical and effective construction progress monitoring.

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Section: Related Workmentioning

confidence: 99%

Plane-Based Robust Registration of a Building Scan with Its BIM

2022

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“…In [9], several algorithms are evaluated following the same procedure: FGR [7], GH-ICP [21] and the algorithm proposed in [9] further called PGR (Planar Global Registration). The results of these experiments are presented in Table II.…”

Section: ) Metricsmentioning

confidence: 99%

“…One way to overcome the point number issue is to use global features, such as planes. In such global registration approaches, the first step is to segment the point clouds into planar patches [7] [8] [9] and then use the planar features to establish correspondences. To match the planar patches, several approaches can be found.…”

Section: Introductionmentioning

confidence: 99%

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Plane-based Accurate Registration of Real-world Point Clouds

Favre

Pressigout

Marchand

et al. 2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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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.

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“…In [16], a plane/line descriptor is proposed to establish structure correspondences. Attributes of the planes and the constraints between them are used in [17].…”

Section: Introductionmentioning

confidence: 99%

A Plane-based Approach for Indoor Point Clouds Registration

Favre

Pressigout

Marchand

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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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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A Fast and Accurate Planar-Feature-Based Global Scan Registration Method

Cited by 15 publications

References 31 publications

Plane-Based Robust Registration of a Building Scan with Its BIM

Plane-Based Robust Registration of a Building Scan with Its BIM

Plane-based Accurate Registration of Real-world Point Clouds

A Plane-based Approach for Indoor Point Clouds Registration

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