文章基本信息

标题：Large Area Inspection Using 3D Point Cloud Data in a Disaster Response Robot
本地全文：下载
作者：Hiroaki Ogawa ; Keito Shishiki ; Udaka A.Manawadu 等
期刊名称：SHS Web of Conferences
印刷版ISSN：2416-5182
电子版ISSN：2261-2424
出版年度：2021
卷号：102
页码：1-5
DOI：10.1051/shsconf/202110204010
语种：English
出版社：EDP Sciences
摘要：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.