Conflicting sensor measurements pose a huge problem for the environment representation of an autonomous robot. Therefore, in this paper, we address the self-assessment of an evidential grid map in which data from conflicting LiDAR sensor measurements are fused, followed by methods for robust motion planning under these circumstances. First, conflicting measurements aggregated in Subjective-Logic-based evidential grid maps are classified. Then, a self-assessment framework evaluates these conflicts and estimates their severity for the overall system by calculating a degradation score. This enables the detection of calibration errors and insufficient sensor setups. In contrast to other motion planning approaches, the information gained from the evidential grid maps is further used inside our proposed path-planning algorithm. Here, the impact of conflicting measurements on the current motion plan is evaluated, and a robust and curious path-planning strategy is derived to plan paths under the influence of conflicting data. This ensures that the system integrity is maintained in severely degraded environment representations which can prevent the unnecessary abortion of planning tasks.

翻译：相互冲突的传感器测量数据对自主机器人的环境表征构成了巨大挑战。为此，本文研究了一种证据栅格地图的自评估方法，该方法首先融合来自冲突激光雷达传感器的测量数据，继而提出在此类情况下的鲁棒运动规划方法。首先，对基于主观逻辑的证据栅格地图中聚合的冲突测量数据进行分类。随后，通过计算退化评分，自评估框架对这些冲突进行评估并估计其对整体系统的严重程度。该方法能够检测标定误差与传感器配置不足的问题。与其他运动规划方法不同，本文进一步将证据栅格地图获得的信息应用于我们提出的路径规划算法中。该算法评估冲突测量数据对当前运动规划的影响，并推导出一种鲁棒且具有探索性的路径规划策略，以在冲突数据影响下规划路径。这确保了在严重退化的环境表征中仍能维持系统完整性，从而避免规划任务被不必要的终止。