High Accuracy Real-Time 6D SLAM with Feature Extraction Using a Neural Network

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カテゴリ: 論文誌(論文単位)グループ名: 【D】産業応用部門（英文）発行日: 2021/09/01タイトル(英語): High Accuracy Real-Time 6D SLAM with Feature Extraction Using a Neural Network著者名: Jiayi Wang (Yokohama National Univers ity), Yasutaka Fujimoto (Yokohama National University)著者名(英語): Jiayi Wang (Yokohama National University), Yasutaka Fujimoto (Yokohama National University)キーワード: robot sensing system，simultaneous localization and mapping，computer vision要約(英語): We present a method for three-dimensional (3D), point cloud-based, six-dimensional (6D) simultaneous localization and mapping (SLAM) with high accuracy and very low computational cost. The two key points of our high accuracy and real-time SLAM process are feature extraction and scan matching optimization. For the 3D laser-based SLAM using the iterative closest point algorithm, we consider correct corresponding point pair searching for achieving high accuracy. Therefore, we propose extracting feature points from 3D point clouds for correct corresponding point pair searching. To extract features such as edges and corners in real-time, we propose the use of a trained neural network (NN). The NN used in our feature extraction scheme is a simple backpropagation (BP) NN with two hidden layers, which allows building a real-time system for 6D SLAM. To optimize the scan matching, we propose the use of particle swam optimization (PSO) and the extracted feature points. The PSO increases the accuracy of the estimated position by matching the most features with a global map stitched with all features. Compared with the state-of-art methods, the proposed method achieved the best performance for the KITTI Odometry Benchmark.本誌: IEEJ Journal of Industry Applications Vol.10 No.5 （2021）本誌掲載ページ: 512-519 p原稿種別: 論文／英語電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejjia/10/5/10_20009366/_article/-char/ja/ Show more