Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Aguiar, AS (Autor) Outra; Filipe Neves Santos (Autor) FEUP; Sobreira, H (Autor) Outra; Boaventura Cunha, J (Autor) Outra; Armando Jorge Sousa (Autor) FEUP

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: FRONTIERS IN ROBOTICS AND AI

Vol. 9

ISSN: 2296-9144

Indexação

ISI Web of Knowledge - 15 Citações

Scopus - 18 Citações

Outras Informações

ID Authenticus: P-00W-0VZ

DOI: 10.3389/frobt.2022.832165

Abstract (EN): Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research and development of localization techniques are essential to boost agricultural robotics. To address this issue, we propose an algorithm called VineSLAM suitable for localization and mapping in agriculture. This approach uses both point- and semiplane-features extracted from 3D LiDAR data to map the environment and localize the robot using a novel Particle Filter that considers both feature modalities. The numeric stability of the algorithm was tested using simulated data. The proposed methodology proved to be suitable to localize a robot using only three orthogonal semiplanes. Moreover, the entire VineSLAM pipeline was compared against a state-of-the-art approach considering three real-world experiments in a woody-crop vineyard. Results show that our approach can localize the robot with precision even in long and symmetric vineyard corridors outperforming the state-of-the-art algorithm in this context.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14