マルチスケールシェル領域の点群占有率に基づく3次元特徴量の提案

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2016/08/01

タイトル(英語): A Proposal of 3D Feature based on Occupancy of Point Cloud in Multi-Scale Shell Region

著者名: 武井　翔一（中京大学大学院 情報科学研究科），秋月　秀一（中京大学大学院 情報科学研究科），橋本　学（中京大学大学院 情報科学研究科）

著者名(英語): Shoichi Takei (Graduate School of Computer and Cognitive Sciences, Chukyo University), Shuichi Akizuki (Graduate School of Computer and Cognitive Sciences, Chukyo University), Manabu Hashimoto (Graduate School of Computer and Cognitive Sciences, Chukyo University)

キーワード: 3次元物体認識，3D特徴記述，占有率，マルチスケールシェル，ロボットビジョン　　3D object recognition，3D feature description，occupancy，multiple scale shell，robot vision

要約(英語): In this paper, we propose a novel keypoint detection and feature description method called “SHORT” (Shell Histograms and Occupancy from Radial Transform) for fast 3D object recognition. Conventional keypoint detection and feature description methods such as the SHOT method have been necessary to calculate many normal vectors or other statistical values from the point cloud data in local regions, so its computational costs are expensive. By contrast, the SHORT method consists of a fast keypoint detector that does not calculate statistics and a fast feature descriptor that uses a small number of points in the restricted local regions. The keypoint detector uses the occupancy measure which can be estimated by only counting the number of points in multiple spherical shell regions. Also the feature descriptor uses a small number of points included in distinctive shell regions of multiple scales. Experimental results in 3D object recognition using real dataset show that the processing speed of the proposed method is approximately nine times faster than that of comparative methods.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.136 No.8 （2016） 特集Ⅰ：知能メカトロニクス分野と連携する知覚情報技術　特集Ⅱ：国際会議ICESS 2015

本誌掲載ページ: 1078-1084 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/136/8/136_1078/_article/-char/ja/