Volumetric maps are widely used in robotics due to their desirable properties in applications such as path planning, exploration, and manipulation. Constant advances in mapping technologies are needed to keep up with the improvements in sensor technology, generating increasingly vast amounts of precise measurements. Handling this data in a computationally and memory-efficient manner is paramount to representing the environment at the desired scales and resolutions. In this work, we express the desirable properties of a volumetric mapping framework through the lens of multi-resolution analysis. This shows that wavelets are a natural foundation for hierarchical and multi-resolution volumetric mapping. Based on this insight we design an efficient mapping system that uses wavelet decomposition. The efficiency of the system enables the use of uncertainty-aware sensor models, improving the quality of the maps. Experiments on both synthetic and real-world data provide mapping accuracy and runtime performance comparisons with state-of-the-art methods on both RGB-D and 3D LiDAR data. The framework is open-sourced to allow the robotics community at large to explore this approach.

翻译：体积地图在机器人学中被广泛应用，因其在路径规划、探索和操作等应用中的理想特性。随着传感器技术的不断进步，需要持续发展地图构建技术以应对日益增长的精确测量数据。以计算和内存高效的方式处理这些数据，对于在所需尺度和分辨率下表示环境至关重要。在本工作中，我们通过多分辨率分析的视角表达体积地图构建框架的理想特性，这表明小波是层次化与多分辨率体积地图构建的自然基础。基于这一见解，我们设计了一个利用小波分解的高效地图构建系统。该系统的效率使得能够使用考虑不确定性的传感器模型，从而提升地图质量。在合成数据与真实数据上的实验，提供了与最先进方法在RGB-D和3D LiDAR数据上的地图精度与运行时间性能比较。该框架已开源，以允许机器人学界广泛探索这一方法。