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

• Type: Article in International Scientific Journal
• Date: 2022

Authors

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

Journal

The Journal is awaiting validation by the Administrative Services.

Title: Front. Robotics and AI

Vol. 9

ISSN: 2296-9144

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 13 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14