SHS Web Conf.
Volume 102, 2021The 3rd ETLTC International Conference on Information and Communications Technology (ETLTC2021)
|Number of page(s)||5|
|Section||Applications in Computer Science|
|Published online||03 May 2021|
Large Area Inspection Using 3D Point Cloud Data in a Disaster Response Robot
Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Japan.
2 Graduate School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Japan.
* e-mail: email@example.com
Large area inspection using a robot is critical in a disastrous situation; especially when humans are inhabiting the catastrophic environment. Unlike natural environments, such environments lack details. Thus, creating 3D maps and identifying objects has became a challenge. This research suggests a 3D Point Cloud Data (PCD) merging algorithm for the less textured environment, aiming World Robot Summit Standard Disaster Robotics Challenge 2021 (WRS). Spider2020, a robotic system designed by the Robot Engineering Laboratory, University of Aizu, was used in this research. Detecting QR codes in a wall and merging PCD, and generating a wall map are the two main tasks in the competition. The Zxing library was used to detect and decode QR codes, and the results were quite accurate. Since the 3D mapping environment has fewer textures, decoded QR code locations are used as the PCD mapping markers. The position of the PCD file was taken from the location given by the robotic arm in Spider2020. The accuracy of merging PCD was improved by including the position of PCD files in the merging algorithm. The robotic system can be used for Large area Inspections in a disastrous situation.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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