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http://dx.doi.org/10.7472/jksii.2022.23.6.71

Improved Parameter Inference for Low-Cost 3D LiDAR-Based Object Detection on Clustering Algorithms  

Kim, Da-hyeon (Dept. of Software, Korea National University of Transportation)
Ahn, Jun-ho (Dept. of Software, Korea National University of Transportation)
Publication Information
Journal of Internet Computing and Services / v.23, no.6, 2022 , pp. 71-78 More about this Journal
Abstract
This paper proposes an algorithm for 3D object detection by processing point cloud data of 3D LiDAR. Unlike 2D LiDAR, 3D LiDAR-based data was too vast and difficult to process in three dimensions. This paper introduces various studies based on 3D LiDAR and describes 3D LiDAR data processing. In this study, we propose a method of processing data of 3D LiDAR using clustering techniques for object detection and design an algorithm that fuses with cameras for clear and accurate 3D object detection. In addition, we study models for clustering 3D LiDAR-based data and study hyperparameter values according to models. When clustering 3D LiDAR-based data, the DBSCAN algorithm showed the most accurate results, and the hyperparameter values of DBSCAN were compared and analyzed. This study will be helpful for object detection research using 3D LiDAR in the future.
Keywords
3D LiDAR; Clustering; Object detection; Fusion camera; Hyper-parameter;
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Times Cited By KSCI : 1  (Citation Analysis)
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