Navigating underground environments using simple topological representations

Cano, Lorenzo (Universidad de Zaragoza) ; Mosteo, Alejandro R. ; Tardioli, Danilo

Resumen: Underground environments are some of the most challenging for autonomous navigation. The long, featureless corridors, loose and slippery soils, bad illumination and unavailability of global localization make many traditional approaches struggle. In this work, a topological-based navigation system is presented that enables autonomous navigation of a ground robot in mine-like environments relying exclusively on a high-level topological representation of the tunnel network. The topological representation is used to generate high-level topological instructions used by the agent to navigate through corridors and intersections. A convolutional neural network (CNN) is used to detect all the galleries accessible to a robot from its current position. The use of a CNN proves to be a reliable approach to this problem, capable of detecting the galleries correctly in a wide variety of situations. The CNN is also able to detect galleries even in the presence of obstacles, which motivates the development of a reactive navigation system that can effectively exploit the predictions of the gallery detection.

Idioma: Inglés

DOI: 10.1109/IROS47612.2022.9981336

Año: 2022

Publicado en: Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems 2022 (2022), 1717-1724
ISSN: 2153-0858

Factor impacto SCIMAGO: 0.853 - Computer Science Applications - Software - Control and Systems Engineering - Computer Vision and Pattern Recognition

Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2019-105390RB-I00

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)