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Real-Time Road Intersection Detection in Sparse Point Cloud Based on Augmented Viewpoints Beam Model
Road intersection is a kind of important navigation landmark, while existing detection methods exhibit clear limitations in terms of their robustness and efficiency. A real-time algorithm for road intersection detection and location in large-scale sparse point clouds is proposed in this paper. Diffe...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650652/ https://www.ncbi.nlm.nih.gov/pubmed/37960553 http://dx.doi.org/10.3390/s23218854 |
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author | Hu, Di Zhang, Kai Yuan, Xia Xu, Jiachen Zhong, Yipan Zhao, Chunxia |
author_facet | Hu, Di Zhang, Kai Yuan, Xia Xu, Jiachen Zhong, Yipan Zhao, Chunxia |
author_sort | Hu, Di |
collection | PubMed |
description | Road intersection is a kind of important navigation landmark, while existing detection methods exhibit clear limitations in terms of their robustness and efficiency. A real-time algorithm for road intersection detection and location in large-scale sparse point clouds is proposed in this paper. Different from traditional approaches, our method establishes the augmented viewpoints beam model to perceive the road bifurcation structure. Explicitly, the spatial features from point clouds are jointly extracted in various viewpoints in front of the robot. In addition, the evaluation metrics are designed to self-assess the quality of detection results, enabling our method to optimize the detection process in real time. Considering the scarcity of datasets for intersection detection, we also collect and annotate a VLP-16 point cloud dataset specifically for intersections, called NCP-Intersection. Quantitative and qualitative experiments demonstrate that the proposed method performs favorably against the other parallel methods. Specifically, our method performs an average precision exceeding 90% and an average processing time of approximately 88 ms/frame. |
format | Online Article Text |
id | pubmed-10650652 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106506522023-10-31 Real-Time Road Intersection Detection in Sparse Point Cloud Based on Augmented Viewpoints Beam Model Hu, Di Zhang, Kai Yuan, Xia Xu, Jiachen Zhong, Yipan Zhao, Chunxia Sensors (Basel) Article Road intersection is a kind of important navigation landmark, while existing detection methods exhibit clear limitations in terms of their robustness and efficiency. A real-time algorithm for road intersection detection and location in large-scale sparse point clouds is proposed in this paper. Different from traditional approaches, our method establishes the augmented viewpoints beam model to perceive the road bifurcation structure. Explicitly, the spatial features from point clouds are jointly extracted in various viewpoints in front of the robot. In addition, the evaluation metrics are designed to self-assess the quality of detection results, enabling our method to optimize the detection process in real time. Considering the scarcity of datasets for intersection detection, we also collect and annotate a VLP-16 point cloud dataset specifically for intersections, called NCP-Intersection. Quantitative and qualitative experiments demonstrate that the proposed method performs favorably against the other parallel methods. Specifically, our method performs an average precision exceeding 90% and an average processing time of approximately 88 ms/frame. MDPI 2023-10-31 /pmc/articles/PMC10650652/ /pubmed/37960553 http://dx.doi.org/10.3390/s23218854 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hu, Di Zhang, Kai Yuan, Xia Xu, Jiachen Zhong, Yipan Zhao, Chunxia Real-Time Road Intersection Detection in Sparse Point Cloud Based on Augmented Viewpoints Beam Model |
title | Real-Time Road Intersection Detection in Sparse Point Cloud Based on Augmented Viewpoints Beam Model |
title_full | Real-Time Road Intersection Detection in Sparse Point Cloud Based on Augmented Viewpoints Beam Model |
title_fullStr | Real-Time Road Intersection Detection in Sparse Point Cloud Based on Augmented Viewpoints Beam Model |
title_full_unstemmed | Real-Time Road Intersection Detection in Sparse Point Cloud Based on Augmented Viewpoints Beam Model |
title_short | Real-Time Road Intersection Detection in Sparse Point Cloud Based on Augmented Viewpoints Beam Model |
title_sort | real-time road intersection detection in sparse point cloud based on augmented viewpoints beam model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650652/ https://www.ncbi.nlm.nih.gov/pubmed/37960553 http://dx.doi.org/10.3390/s23218854 |
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