通过自评估融合激光雷达数据，提升机器人在混乱环境下的路径规划鲁棒性。
Self-Assessment of Evidential Grid Map Fusion for Robust Motion Planning
- 用主观逻辑构建证据网格地图，识别冲突传感器数据
- 计算退化分数评估冲突严重性，发现校准与部署问题
- 基于冲突影响动态调整路径策略，适合复杂场景机器人
冲突的传感器测量严重影响自主机器人环境建模。本文提出一种证据网格地图的自评估方法，融合来自冲突激光雷达的数据，并在此基础上实现鲁棒运动规划。首先，基于主观逻辑的证据网格地图对冲突测量进行分类；随后，通过自评估框架计算退化分数，量化冲突对系统整体的影响，从而检测校准误差和传感器配置不足。与传统方法不同，本文提出的路径规划算法进一步利用证据网格地图中的信息，评估当前冲突测量对路径规划的影响，并生成具有鲁棒性和探索性的路径规划策略，确保在严重退化的环境表示下仍能维持系统完整性，避免规划任务无谓中止。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。