비정형의 건설환경 매핑을 위한 레이저 반사광 강도와 주변광을 활용한 향상된 라이다-관성 슬램Intensity and Ambient Enhanced Lidar-Inertial SLAM for Unstructured Construction Environment

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Construction monitoring is one of the key modules in smart construction. Unlike structured urban environment, construction site mapping is challenging due to the characteristics of an unstructured environment. For example, irregular feature points and matching prohibit creating a map for management. To tackle this issue, we propose a system for data acquisition in unstructured environment and a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping, IA-LIO-SAM, that achieves highly accurate robot trajectories and mapping. IA-LIO-SAM utilizes a factor graph same as Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (LIO-SAM). Enhancing the existing LIO-SAM, IA-LIO-SAM leverages point’s intensity and ambient value to remove unnecessary feature points. These additional values also perform as a new factor of the K-Nearest Neighbor algorithm (KNN), allowing accurate comparisons between stored points and scanned points. The performance was verified in three different environments and compared with LIO-SAM.
Publisher
한국로봇학회
Issue Date
2021
Language
Korean
Citation

로봇학회 논문지, v.16, no.3, pp.179 - 188

ISSN
1975-6291
DOI
10.7746/jkros.2021.16.3.179
URI
http://hdl.handle.net/10203/290570
Appears in Collection
CE-Journal Papers(저널논문)
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