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학술대회 Anomaly Detection using Elevation and Thermal Map for Security Robot
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신호철, 나기인
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1760-1762
20HS3500, 실외 무인 경비 로봇을 위한 멀티모달 지능형 정보분석 기술 개발, 신호철
This paper is about an algorithm that displays the elevation and temperature of a surveillance area in the form of a map using a mobile robot platform equipped with a Lidar and a thermal imaging camera, and determines whether it is abnormal. Localization is performed using Lidar, and the point cloud and thermal distribution at the corresponding location are expressed as a top projection type map. The collected elevation and temperature distribution maps are checked for anomalies using the first-stage auto-encoder and second-stage CNN algorithm. We collected data on normal situations and abnormal situation DB produced by various size boxes and gas burners to study. As a result of the study, it was possible to detect 74.4% for the first stage, 94.5% for the second stage, and 80.2% for the first stage and 99.3% for the second stage for the thermal map. The results of this study will be used in areas such as security robots and guide robots.
KSP 제안 키워드
Abnormal situation, Auto-Encoder(AE), CNN algorithm, First stage, Mobile Robot Platform, Point clouds, Second-stage, Security Robots, Temperature Distribution, Thermal Imaging Camera, Thermal distribution