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.
Highlights
The development of autonomous robots in agriculture is a challenging and active research topic (Emmi et al, 2014)
We propose VineSLAM, a 6-DoF Simultaneous Localization and Mapping (SLAM) algorithm for agricultural environments that uses point and semiplane features extracted from 3D point clouds
This work proposes an extension of the state-of-the-art in localization and mapping oriented to agricultural robots
Summary
The development of autonomous robots in agriculture is a challenging and active research topic (Emmi et al, 2014) To implement such systems, the autonomous navigation issue must be solved, i.e., robots should be capable of driving autonomously within multiple environments Shalal et al (2013). The implementation of SLAM is important since it leads to creating maps that farmers can use in various tasks. When robots have this ability, they can perform several autonomous operations such as precision agriculture (application of fertilizers, nutrients and water), plant protection, harvesting, monitoring, and planting (Bergerman et al, 2016; Roldán et al, 2018; Pinto de Aguiar et al, 2020). The implementation of SLAM in outdoor agricultural environments can be challenging since the characteristics of illumination and terrain
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