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http://dx.doi.org/10.14372/IEMEK.2021.16.3.107

Development of 3D Point Cloud Mapping System Using 2D LiDAR and Commercial Visual-inertial Odometry Sensor  

Moon, Jongsik (Nearthlab)
Lee, Byung-Yoon (Nearthlab)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.16, no.3, 2021 , pp. 107-111 More about this Journal
Abstract
A 3D point cloud map is an essential elements in various fields, including precise autonomous navigation system. However, generating a 3D point cloud map using a single sensor has limitations due to the price of expensive sensor. In order to solve this problem, we propose a precise 3D mapping system using low-cost sensor fusion. Generating a point cloud map requires the process of estimating the current position and attitude, and describing the surrounding environment. In this paper, we utilized a commercial visual-inertial odometry sensor to estimate the current position and attitude states. Based on the state value, the 2D LiDAR measurement values describe the surrounding environment to create a point cloud map. To analyze the performance of the proposed algorithm, we compared the performance of the proposed algorithm and the 3D LiDAR-based SLAM (simultaneous localization and mapping) algorithm. As a result, it was confirmed that a precise 3D point cloud map can be generated with the low-cost sensor fusion system proposed in this paper.
Keywords
SLAM; Sensor Fusion; LiDAR sensor; Mapping; Vision sensor;
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