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. Videos and code are available at: https://samsunglabs.github.io/HIO-SDF-project-page/

翻译：大型复杂移动机器人工作空间的良好表示必须既节省空间，又能编码相关几何细节。在探索未知环境时，它需要能够以在线方式增量更新。我们提出 HIO-SDF，一种将环境表示为有符号距离场的新方法。当前最先进的有符号距离场表示方法基于神经网络或体素网格。神经网络能够连续表示有符号距离场，但难以增量更新，因为神经网络容易遗忘环境先前观测到的部分，除非存储大量传感器历史数据进行训练。基于体素的表示无此问题，但在包含精细细节的大型环境中空间效率低下。HIO-SDF 通过分层方法结合了这些表示的优势：采用粗粒度体素网格捕获环境观测区域，并利用高分辨率局部信息训练神经网络。在所有测试场景中，HIO-SDF 的平均全局有符号距离场误差比最先进的连续表示低 46%，比与粗粒度全局有符号距离场网格相同分辨率的离散表示低 30%。视频和代码见：https://samsunglabs.github.io/HIO-SDF-project-page/