OpenStreetMap-Based Autonomous Navigation With LiDAR Naive-Valley-Path Obstacle Avoidance
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/128272
Título: | OpenStreetMap-Based Autonomous Navigation With LiDAR Naive-Valley-Path Obstacle Avoidance |
---|---|
Autor/es: | Muñoz-Bañón, Miguel Á. | Velasco, Edison P. | Candelas-Herías, Francisco A. | Torres, Fernando |
Grupo/s de investigación o GITE: | Automática, Robótica y Visión Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal |
Palabras clave: | Autonomous navigation | Unmanned ground vehicle | Open street maps | Path planning | Obstacle avoidance | LiDAR point cloud |
Fecha de publicación: | 30-sep-2022 |
Editor: | IEEE |
Cita bibliográfica: | IEEE Transactions on Intelligent Transportation Systems. 2022, 23(12): 24428-24438. https://doi.org/10.1109/TITS.2022.3208829 |
Resumen: | OpenStreetMaps (OSM) is currently studied as the environment representation for autonomous navigation. It provides advantages such as global consistency, a heavy-less map construction process, and a wide variety of road information publicly available. However, the location of this information is usually not very accurate locally. In this paper, we present a complete autonomous navigation pipeline using OSM information as environment representation for global planning. To avoid the flaw of local low-accuracy, we offer the novel LiDAR-based Naive-Valley-Path (NVP) method that exploits the concept of “valley” areas to infer the local path always furthest from obstacles. This behavior allows navigation always through the center of trafficable areas following the road’s shape independently of OSM error. Furthermore, NVP is a naive method that is highly sample-time-efficient. This time efficiency also enables obstacle avoidance, even for dynamic objects. We demonstrate the system’s robustness in our research platform BLUE, driving autonomously across the University of Alicante Scientific Park for more than 20 km with 0.24 meters of average error against the road’s center with a 19.8 ms of average sample time. Our vehicle avoids static obstacles in the road and even dynamic ones, such as vehicles and pedestrians. |
Patrocinador/es: | This work was supported in part by the Spanish Government through the Formación del Personal Investigador [Research Staff Formation (FPI)] under Grant PRE2019-088069, in part by the Research Project under Grant RTI2018-094279-B-100, and in part by the Regional Valencian Community Government and the European Regional Development Fund (ERDF) under Grant ACIF/2019/088. |
URI: | http://hdl.handle.net/10045/128272 |
ISSN: | 1524-9050 (Print) | 1558-0016 (Online) |
DOI: | 10.1109/TITS.2022.3208829 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1109/TITS.2022.3208829 |
Aparece en las colecciones: | INV - AUROVA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
Munoz-Banon_etal_2022_IEEE-TITS_accepted.pdf | Accepted Manuscript (acceso abierto) | 3,14 MB | Adobe PDF | Abrir Vista previa |
Munoz-Banon_etal_2022_IEEE-TITS_final.pdf | Versión final (acceso restringido) | 2,99 MB | Adobe PDF | Abrir Solicitar una copia |
Todos los documentos en RUA están protegidos por derechos de autor. Algunos derechos reservados.