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

Aguiar, André Silva and Neves dos Santos, Filipe and Sobreira, Héber and Boaventura-Cunha, José and Sousa, Armando Jorge (2022) Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data. Frontiers in Robotics and AI, 9. ISSN 2296-9144

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Abstract

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.

Item Type: Article
Subjects: Apsci Archives > Mathematical Science
Depositing User: Unnamed user with email support@apsciarchives.com
Date Deposited: 23 Jun 2023 05:40
Last Modified: 01 Nov 2023 05:28
URI: http://eprints.go2submission.com/id/eprint/1408

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