置信度高的网格映射
Confidence-rich grid mapping
摘要：
表示环境是使机器人能够在未知环境中自主行动的基本任务。在这项工作中，我们提出了信心丰富的映射（CRM），一个新的算法，空间网格为基础的映射的三维环境。CRM通过其置信度值增加每个体素处的占用水平。通过使用CRM过滤器显式地存储和演化置信度值，CRM以三种方式扩展了传统的网格映射：首先，它部分地保持体素之间的概率依赖性。其次，它放松了手工设计反向传感器模型的需要，并提出了传感器原因模型的概念，该模型可以从正向传感器模型中以原则性的方式导出。第三，最重要的是，它提供了一致的置信度值的占用估计，可以可靠地用于碰撞风险评估和运动规划。CRM在线运行，并启用可能部分占用体素的映射环境。我们展示了该方法在模拟和物理系统中的各种数据集和环境上的性能。我们在现实世界的实验中表明，除了实现比传统方法更准确的地图，建议的过滤方案表现出更高的水平的一致性之间的错误和报告的信心，因此，使一个更可靠的碰撞风险评估运动规划。
英文摘要：
Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments. In this work, we present confidence-rich mapping (CRM), a new algorithm for spatial grid-based mapping of the 3D environment. CRM augments the occupancy level at each voxel by its confidence value. By explicitly storing and evolving confidence values using the CRM filter, CRM extends traditional grid mapping in three ways: first, it partially maintains the probabilistic dependence among voxels. Second, it relaxes the need for hand-engineering an inverse sensor model and proposes the concept of sensor cause model that can be derived in a principled manner from the forward sensor model. Third, and most importantly, it provides consistent confidence values over the occupancy estimation that can be reliably used in collision risk evaluation and motion planning. CRM runs online and enables mapping environments where voxels might be partially occupied. We demonstrate the performance of the method on various datasets and environments in simulation and on physical systems. We show in real-world experiments that, in addition to achieving maps that are more accurate than traditional methods, the proposed filtering scheme demonstrates a much higher level of consistency between its error and the reported confidence, hence, enabling a more reliable collision risk evaluation for motion planning.
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