A good representation of a large, complex mobile robot workspace must be space-efficient yet capable of encoding relevant geometric details. When exploring unknown environments, it needs to be updatable incrementally in an online fashion. We introduce HIO-SDF, a new method that represents the environment as a Signed Distance Field (SDF). State of the art representations of SDFs are based on either neural networks or voxel grids. Neural networks are capable of representing the SDF continuously. However, they are hard to update incrementally as neural networks tend to forget previously observed parts of the environment unless an extensive sensor history is stored for training. Voxel-based representations do not have this problem but they are not space-efficient especially in large environments with fine details. HIO-SDF combines the advantages of these representations using a hierarchical approach which employs a coarse voxel grid that captures the observed parts of the environment together with high-resolution local information to train a neural network. HIO-SDF achieves a 46% lower mean global SDF error across all test scenes than a state of the art continuous representation, and a 30% lower error than a discrete representation at the same resolution as our coarse global SDF grid.

翻译：针对大型、复杂移动机器人工作空间的良好表示需兼顾空间效率与相关几何细节的编码能力。在探索未知环境时，该表示需支持在线增量更新。本文提出HIO-SDF，一种将环境表示为有符号距离场（SDF）的新方法。现有最优的SDF表示方法基于神经网络或体素网格：神经网络能连续表示SDF，但难以实现增量更新——除非存储大量传感器历史数据用于训练，否则神经网络易遗忘先前观测到的环境部分；基于体素的表示虽不存在此问题，但空间效率低下，尤其在存在精细细节的大型环境中。HIO-SDF通过分层方法结合二者优势：采用粗体素网格捕获已观测环境部分，并结合高分辨率局部信息训练神经网络。与最优连续表示方法相比，HIO-SDF在所有测试场景中的全局平均SDF误差降低了46%；与分辨率等同于粗全局SDF网格的离散表示方法相比，其误差降低了30%。