UFOMap: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown
This work addresses the need for efficient probabilistic 3D mapping in robotic applications like path planning and exploration, where environments are unknown or partially known, representing an incremental improvement over existing methods.
The paper tackles the problem of efficiently representing unknown space in 3D mapping for robotics by extending OctoMap to explicitly handle occupied, free, and unknown states, resulting in a more memory-efficient representation and significantly faster insertions enabling real-time colored volumetric mapping at high resolution (below 1 cm).
3D models are an essential part of many robotic applications. In applications where the environment is unknown a-priori, or where only a part of the environment is known, it is important that the 3D model can handle the unknown space efficiently. Path planning, exploration, and reconstruction all fall into this category. In this paper we present an extension to OctoMap which we call UFOMap. UFOMap uses an explicit representation of all three states in the map, i.e., occupied, free, and unknown. This gives, surprisingly, a more memory efficient representation. Furthermore, we provide methods that allow for significantly faster insertions into the octree. This enables real-time colored volumetric mapping at high resolution (below 1 cm). UFOMap is contributed as a C++ library that can be used standalone but is also integrated into ROS.