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.

中文翻译：

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置信度高的网格映射

代表环境是使机器人能够在未知环境中自主行动的一项基本任务。在这项工作中，我们提出了置信度丰富的映射 (CRM)，这是一种基于空间网格的 3D 环境映射的新算法。CRM 通过其置信值增加每个体素的占用水平。通过使用 CRM 过滤器显式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，它部分地保持了体素之间的概率依赖性。其次，它放宽了手动设计逆传感器模型的需要，并提出了传感器原因模型的概念，该概念可以从正传感器模型中以原则方式推导出来。第三，也是最重要的，它为占用估计提供一致的置信值，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，除了获得比传统方法更准确的地图外，所提出的过滤方案还展示了其错误与报告置信度之间的更高水平的一致性，因此，能够进行更可靠的碰撞风险评估用于运动规划。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，除了获得比传统方法更准确的地图外，所提出的过滤方案还展示了其错误与报告置信度之间的更高水平的一致性，因此，能够进行更可靠的碰撞风险评估用于运动规划。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，除了获得比传统方法更准确的地图外，所提出的过滤方案还展示了其错误与报告置信度之间的更高水平的一致性，因此，能够进行更可靠的碰撞风险评估用于运动规划。