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Performance Improvement of ICP-SLAM by Human Removal Process Using YOLO
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Keigo AKIBA1) Ryuki SUZUKI1) Yonghoon JI2) Sarthak PATHAK3) Kazunori UMEDA3)
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1) School of Science and Engineering, Chuo University {akiba}@sensor.mech.chuo-u.ac.jp
2) School of Materials Science/Intelligent Robotics Area, Japan Advanced Institute of Science and Technology (JAIST), Japan
3) Faculty of Science and Engineering, Chuo University, Japan
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Received: Jul. 20 2022; Revised: Dec 11 2022; Accepted: Feb. 8 2023
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Keywords: SLAM, ICP, mobile robot, human removal process, YOLO, map construction
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| Abstract | |
| In this paper, we propose a novel iterative closest point (ICP)-based simultaneous localization and mapping (SLAM) approach that can build robust map infor-mation even in indoor environments where humans coexist. Several SLAM methods that have been studied so far assume a stationary environment. But there are challenges in operating in a dynamic environment with moving objects such as humans. Specifically, when a mobile robot constructs a map in an environ-ment where humans coexist nearby, humans cause false matching in alignment sensor data. Furthermore, human occlusion also makes it difficult to construct a map with high accuracy. Therefore, we propose a human removal process that utilizes You Look Only Once (YOLO) to detect humans in image data. In this paper, by using this process with ICP-SLAM, we aim to improve the accuracy of map construction in an environment where humans coexist nearby. In our exper-iments, we verified the accuracy of map construction in comparison with conven-tional methods. This experiment is conducted in an indoor corridor where hu-mans coexist nearby. Although we used ICP-SLAM for verification this time, the human removal process can be adapted to other SLAMS.
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| Copyright © 2019 The Academy of Human Informatics |
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