VDB-Mapping: A High Resolution and Real-Time Capable 3D Mapping Framework for Versatile Mobile Robots

Recent developments of versatile mobile robots demand a precise and complete volumetric representation of space for path and mission planning. However, the immense amount of accumulated raw data points from 3D sensors quickly becomes unmanageable, thereby becoming infeasible regarding memory footprint and navigation itself. To abstract the necessary information into a usable representation for navigation, usually volumetric grid maps are applied. Although these approaches solve the memory and handling issues, current implementations tend to require high computational time for the insertion of new data. As a result the generated maps are often not up to date and incomplete due to dropped sensor data. This greatly impairs their usefulness for navigating robot systems in dynamically changing environments. In order to solve these issues we propose a novel probabilistic mapping framework based on OpenVDB, which is a hierarchical tree structure with efficient access methods to discretized volumetric data. By utilizing the fast direct access to the bitmasks of the underlying OpenVDB data structure, a significant performance boost for data insertion is achieved. Thus, enabling real-time processing of the incoming raw 3D sensor data. An in-depth evaluation of the proposed framework is provided, including a performance and memory footprint comparison against the often employed OctoMap framework. The evaluation reveals, that the presented VDB-Mapping is able to efficiently process long range data on high resolution grids.