Path-Tracking Control Based on Deep ORB-SLAM2

Kai Tai Song, Song Qing Ou

研究成果: Conference contribution同行評審

摘要

This paper proposes a mobile robot navigation control design based on deep vSLAM approach. The deep vSLAM system integrates ORB-SLAM2 with a deep neural network (DNN) to enhance the localization performance of an embedded navigation system. An RGB-D camera is utilized to realize the deep vSLAM system. In the proposed deep vSLAM algorithm, the DNN works to accelerate feature matching and feature detection steps in the original ORB-SLAM2. Further, we developed a path-tracking controller to navigate the robot on the planned path based on the vSLAM localization. Several interesting experiments are presented to validate the performance of the proposed method. The experimental results on a mobile robot show that the computation time of the DNN based feature matching is reduced to 45.16% of that of the original ORB-SLAM2, and the path-tracking error of a squared-shape path of 19.8m is within 20mm.

原文English
主出版物標題2020 International Automatic Control Conference, CACS 2020
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728171982
DOIs
出版狀態Published - 4 十一月 2020
事件2020 International Automatic Control Conference, CACS 2020 - Hsinchu, Taiwan
持續時間: 4 十一月 20207 十一月 2020

出版系列

名字2020 International Automatic Control Conference, CACS 2020

Conference

Conference2020 International Automatic Control Conference, CACS 2020
國家/地區Taiwan
城市Hsinchu
期間4/11/207/11/20

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