Journal of Advances in Artificial Life Robotics
Online ISSN : 2435-8061
ISSN-L : 2435-8061
Deep Learning Methods for Robotic Arm Workspace Scene Reconstruction
Pei YingjianSakmongkon ChumkamonEiji Hayashi
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JOURNAL OPEN ACCESS

2021 Volume 2 Issue 1 Pages 22-26

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Abstract
This research is part of the Yaskawa Motoman Robot Autonomous Control Project, which aims to map the real workspace in a virtual environment using a depth camera mounted on the robot, and to plan the robot's autonomous obstacle avoidance path based on the 3D octomap. The main tool used in this study is RTAB-Map, which is based on the built-in handheld mapping scheme to improve it to meet our actual needs. After the actual test, our solution shows finer mapping accuracy, can update the map data in real time, and the perception of obstacles within the field of view is more comprehensive, but there is still a lot of room for optimizing the mapping speed.
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© 2021 ALife Robotics Corporation Ltd.

この記事はクリエイティブ・コモンズ [表示 - 非営利 4.0 国際]ライセンスの下に提供されています。
https://creativecommons.org/licenses/by-nc/4.0/deed.ja
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