Point cloud to BIM registration for robot localization and Augmented Reality

Schaub, Linus; Podkosova, Iana; Schönauer, Christian; Kaufmann, Hannes

doi:10.1109/ismar-adjunct57072.2022.00025

Cited by 2 publications

(4 citation statements)

References 19 publications

(36 reference statements)

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“…Similar to several other approaches, the localization procedure necessitates an initial pose as a starting parameter for the algorithm [ 36 , 40 , 41 ]. Contrasting with the plane extraction technique in [ 27 , 28 ], the method proposed herein capitalizes on known map information to extract plane point clouds.…”

Section: Discussionmentioning

confidence: 99%

“…Plane matching correctness relies on map complexity and historical motion patterns, not camera accuracy–a notable advantage over Schaub et al . [ 36 ]. In instances where two planes exhibiting similar angles and distances occupy the field of view, identification errors might arise.…”

Section: Discussionmentioning

confidence: 99%

“…Building Information Modeling (BIM) technology enhances the informativeness, intelligence, and eco-friendliness of the construction industry [ 35 ]. Numerous studies have explored the integration of a-priori BIM blueprint data with mobile robot localization [ 1 , 9 , 36 ]. The a-priori plane information of a construction site is readily accessible from the digital model [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

“…Schaub et al . [ 36 ] localized mobile robots by matching the entire point cloud in the scene to the complete point cloud generated by BIM. Nonetheless, matching entire point clouds necessitates high-quality online point cloud observations.…”

Section: Introductionmentioning

confidence: 99%

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Localization on a-priori information of plane extraction

Zhao

Misyurin

et al. 2023

PLoS ONE

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Localization constitutes a critical challenge for autonomous mobile robots, with flattened walls serving as a fundamental reference for indoor localization. In numerous scenarios, prior knowledge of a wall’s surface plane is available, such as planes in building information modeling (BIM) systems. This article presents a localization technique based on a-priori plane point cloud extraction. The position and pose of the mobile robot are estimated through real-time multi-plane constraints. An extended image coordinate system is proposed to represent any planes in space and establish correspondences between visible planes and those in the world coordinate system. Potentially visible points representing the constrained plane in the real-time point cloud are filtered using the filter region of interest (ROI), derived from the theoretical visible plane region within the extended image coordinate system. The number of points representing the plane influences the calculation weight in the multi-plane localization approach. Experimental validation of the proposed localization method demonstrates its allowance for redundancy in initial position and pose error.

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