Research on clustering of non-uniformly distributed point clouds in road scenes
Pages 376 - 381
Abstract
This paper presents a clustering algorithm for non-uniformly distributed point clouds in road scenes, which is used to alleviate the performance effect of classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm in non-uniformly distributed scenes. Because of the limitations of the DBSCAN algorithm, it's difficult to show good results in the space where the parameters aren't convergent. So we proposes a solution, which calculates the node density, average density, density variation coefficient and other parameters of each point which is divided the space into several small spaces with uniform density. Through this method we can achieve better clustering effect in small spaces. Finally, we analyze the proposed solution through Python code on some KITTI data sets. The analysis results show that our proposed scheme can effectively improve the performance of classical DBSCAN algorithm in non-density uniform space.
References
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Published In
February 2024
757 pages
ISBN:9798400709234
DOI:10.1145/3651671
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Published: 07 June 2024
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ICMLC 2024
ICMLC 2024: 2024 16th International Conference on Machine Learning and Computing
February 2 - 5, 2024
Shenzhen, China
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