Developing safe autonomous robotic applications for outdoor agricultural environments is a research field that still presents many challenges. Simultaneous Localization and Mapping can be crucial to endow the robot to localize itself with accuracy and, consequently, perform tasks such as crop monitoring and harvesting autonomously. In these environments, the robotic localization and mapping systems usually benefit from the high density of visual features. When using filter-based solutions to localize the robot, such an environment usually uses a high number of particles to perform accurately. These two facts can lead to computationally expensive localization algorithms that are intended to perform in real-time. This work proposes a refinement step to a standard high-dimensional filter-based localization solution through the novelty of downsampling the filter using an online clustering algorithm and applying a scan-match procedure to each cluster. Thus, this approach allows scan-matchers without high computational cost, even in high dimensional filters. Experiments using real data in an agricultural environment show that this approach improves the Particle Filter performance estimating the robot pose. Additionally, results show that this approach can build a precise 3D reconstruction of agricultural environments using visual scans, i.e., 3D scans with RGB information.

为户外农业环境开发安全的自主机器人应用程序是一个仍然存在许多挑战的研究领域。同时进行本地化和制图对于使机器人能够准确地对其自身进行本地化并因此自动执行诸如作物监测和收获之类的任务至关重要。在这些环境中，机器人定位和制图系统通常受益于视觉特征的高密度。当使用基于过滤器的解决方案来定位机器人时，这种环境通常使用大量的粒子来精确地执行操作。这两个事实可能导致旨在实时执行的计算量大的本地化算法。通过使用在线聚类算法对滤波器进行下采样并对每个聚类应用扫描匹配过程的新颖性，这项工作提出了对基于高维滤波器的标准定位解决方案的改进步骤。因此，即使在高维滤波器中，该方法也允许扫描匹配器没有高计算成本。在农业环境中使用实际数据进行的实验表明，这种方法提高了估计机器人姿势的粒子过滤器的性能。此外，结果表明，该方法可以使用以下方法建立农业环境的精确3D重建：在农业环境中使用实际数据进行的实验表明，这种方法提高了估计机器人姿势的粒子过滤器的性能。此外，结果表明，该方法可以使用以下方法建立农业环境的精确3D重建：在农业环境中使用实际数据进行的实验表明，这种方法提高了估计机器人姿势的粒子过滤器的性能。此外，结果表明，该方法可以使用以下方法建立农业环境的精确3D重建：视觉扫描，即具有RGB信息的3D扫描。