@techreport{oai:ipsj.ixsq.nii.ac.jp:00231331,
 author = {岩川, 明央 and 柴田, 泰邦 and Haruchika, Iwakawa and Yasukuni, Shibata},
 issue = {5},
 month = {Nov},
 note = {視覚に何らかの障碍を持つ人々の数は世界で 22 億人を超え，在宅の障碍者が増加している．一方で，障害物検知技術の多くが cm から m 単位であるのに対し，国土交通省のガイドラインによれば，車椅子移動時に障害となる段差は 5mm 以上と定められている．この課題を解決するために，本研究では ToF 方式の RBG-深度カメラを用いて，在宅の障碍を持つ人々の生活支援を行うことを目指し，RBG-深度カメラで得られた点群データから 5mm 以上のバリアを検知する手法を提案する．さらに，段差モデルを用いた検知精度の検証実験を行い，従来法と比較して検出分解能・精度およびリアルタイム性が飛躍的に向上し，提案手法の妥当性を実証した．, More than 2.2 billion people worldwide have some form of visual impairment, and the number of homebound people with disabilities is increasing. On the other hand, while most obstacle detection technologies are based on cm to m units, th e Ministry of Land, Infrastructure, Transport and Tourism (MLIT) guidelines stipulate that the minimum difference between steps that would be the minimum obstacle height for wheelchair users is 5 mm or more. To solve this problem, we propose a method to detect barriers of 5 mm or more from point cloud data obtained by a ToF-based RBG-depth camera, aiming to support the daily lives of physically challenged people at home. Furthermore, we conducted experiments to verify the detection accuracy using a step model, and demonstrated that the proposed method dramatically improves the detection resolution, accuracy, and real-time performance compared to conventional methods, validating the effectiveness of the proposed method.},
 title = {車いす利用者向けのRGB-深度カメラを使用したバリア検出手法の開発},
 year = {2023}
}