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.