SLAM-driven robotic mapping and registration of 3D point clouds

SLAM-driven robotic mapping and registration of 3D point clouds With the rapid advancement of laser scanning and photogrammetry technologies, frequent geometric data collection at construction sites by contractors has been increased for the purpose of improving constructability, productivity, and onsite safety. However, the conventional static laser scanning method suffers from operational limitations due to the presence of many occlusions commonly found in a typical construction site. Obtaining a complete scan of a construction site without information loss requires that laser scans are obtained from multiple scanning locations around the site, which also necessitates extra work for registering each scanned point cloud. As an alternate solution to this problem, this paper introduces an autonomous mobile robot which navigates a scan site based on a continuously updated point cloud map. This mobile robot system utilizes the 2D Hector Simultaneous Localization and Mapping (SLAM) technique to estimate real-time positions and orientations of the robot in the x-y plane. Then, the 2D localization information is used to create 3D point clouds of unknown environments in real time to determine its navigation paths as a pre-scanning process. The advantage of this framework is the ability to determine the optimal scan position and scan angle to reduce the scanning time and effort for gathering high resolution point cloud data in real-time. The mobile robot system is able to capture survey-quality RGB-mapped point cloud data, and automatically register the scans for geometric reconstruction of the site. The performance of the overall system was tested in an indoor environment and validated with promising results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automation in Construction Elsevier

SLAM-driven robotic mapping and registration of 3D point clouds

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier B.V.
ISSN
0926-5805
D.O.I.
10.1016/j.autcon.2018.01.009
Publisher site
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Abstract

With the rapid advancement of laser scanning and photogrammetry technologies, frequent geometric data collection at construction sites by contractors has been increased for the purpose of improving constructability, productivity, and onsite safety. However, the conventional static laser scanning method suffers from operational limitations due to the presence of many occlusions commonly found in a typical construction site. Obtaining a complete scan of a construction site without information loss requires that laser scans are obtained from multiple scanning locations around the site, which also necessitates extra work for registering each scanned point cloud. As an alternate solution to this problem, this paper introduces an autonomous mobile robot which navigates a scan site based on a continuously updated point cloud map. This mobile robot system utilizes the 2D Hector Simultaneous Localization and Mapping (SLAM) technique to estimate real-time positions and orientations of the robot in the x-y plane. Then, the 2D localization information is used to create 3D point clouds of unknown environments in real time to determine its navigation paths as a pre-scanning process. The advantage of this framework is the ability to determine the optimal scan position and scan angle to reduce the scanning time and effort for gathering high resolution point cloud data in real-time. The mobile robot system is able to capture survey-quality RGB-mapped point cloud data, and automatically register the scans for geometric reconstruction of the site. The performance of the overall system was tested in an indoor environment and validated with promising results.

Journal

Automation in ConstructionElsevier

Published: May 1, 2018

References

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