Mobile robots designed for agricultural tasks need to deal with challenging outdoor unstructured environments that usually have dynamic and static obstacles. This assumption significantly limits the number of mapping, path planning, and navigation algorithms to be used in this application. As a representative case, the autonomous lawn mowing robot considered in this work is required to determine the working area and to detect obstacles simultaneously, which is a key feature for its working efficiency and safety. In this context, RGB-D cameras are the optimal solution, providing a scene image including depth data with a compromise between precision and sensor cost. For this reason, the obstacle detection effectiveness and precision depend significantly on the sensors used, and the information processing approach has an impact on the avoidance performance. The study presented in this work aims to determine the obstacle mapping accuracy considering both hardware- and information processing-related uncertainties. The proposed evaluation is based on artificial and real data to compute the accuracy-related performance metrics. The results show that the proposed image and depth data processing pipeline introduces an additional distortion of 38 cm.

中文翻译：

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使用RGB-D相机的农业移动机器人应用的bstacle检测系统

为农业任务设计的移动机器人需要应对通常具有动态和静态障碍物的具有挑战性的室外非结构化环境。这一假设极大地限制了本应用中要使用的地图绘制、路径规划和导航算法的数量。作为一个代表性案例，本工作中考虑的自主割草机器人需要确定工作区域并同时检测障碍物，这是其工作效率和安全性的关键特征。在这种情况下，RGB-D 相机是最佳解决方案，它提供包含深度数据的场景图像，同时兼顾精度和传感器成本。因此，障碍物检测的有效性和精度在很大程度上取决于所使用的传感器，信息处理方式对回避性能有影响。这项工作中提出的研究旨在确定与硬件和信息处理相关的不确定性的障碍物映射精度。建议的评估基于人工和真实数据来计算与准确性相关的性能指标。结果表明，所提出的图像和深度数据处理管道引入了 38 cm 的额外失真。