Aguiar André Silva, Neves Dos Santos Filipe, Sobreira Héber, Boaventura-Cunha José, Sousa Armando Jorge
INESC TEC-INESC Technology and Science, Porto, Portugal.
School of Science and Technology, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal.
Front Robot AI. 2022 Jan 28;9:832165. doi: 10.3389/frobt.2022.832165. eCollection 2022.
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.
开发用于农业的地面机器人是一项艰巨的任务。机器人应能够执行诸如喷洒、收割或监测等任务。然而，农业场景中缺乏结构对定位和映射算法的实施提出了挑战。因此，定位技术的研发对于推动农业机器人技术至关重要。为了解决这个问题，我们提出了一种名为VineSLAM的算法，适用于农业中的定位和映射。这种方法使用从3D激光雷达数据中提取的点特征和半平面特征来绘制环境地图，并使用一种考虑两种特征模态的新型粒子滤波器对机器人进行定位。使用模拟数据测试了该算法的数值稳定性。所提出的方法被证明仅使用三个正交半平面就适合对机器人进行定位。此外，在一个木本作物葡萄园进行的三个实际实验中，将整个VineSLAM管道与一种先进方法进行了比较。结果表明，我们的方法即使在长且对称的葡萄园走廊中也能精确地对机器人进行定位，在这种情况下优于先进算法。
