# UFOMap: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown

arXiv: https://arxiv.org/abs/2003.04749 · HTML: https://mlsanity.com/paper/2003.04749/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [DUFOMap: Efficient Dynamic Awareness Mapping](https://mlsanity.com/paper/2403.01449/) (2024)
- [Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models](https://mlsanity.com/paper/2302.00047/) (2023)
- [BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps](https://mlsanity.com/paper/2405.07283/) (2024)
- [Social 3D Scene Graphs: Modeling Human Actions and Relations for Interactive Service Robots](https://mlsanity.com/paper/2509.24966/) (2025)
- [STAGE: Scalable and Traversability-Aware Graph based Exploration Planner for Dynamically Varying Environments](https://mlsanity.com/paper/2402.02566/) (2024)
- [GIRA: Gaussian Mixture Models for Inference and Robot Autonomy](https://mlsanity.com/paper/2307.00071/) (2023)
- [OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown Environments](https://mlsanity.com/paper/2406.07294/) (2024)
- [Next-Best-Trajectory Planning of Robot Manipulators for Effective Observation and Exploration](https://mlsanity.com/paper/2503.22588/) (2025)
- [REST: Receding Horizon Explorative Steiner Tree for Zero-Shot Object-Goal Navigation](https://mlsanity.com/paper/2603.18624/) (2026)
- [A Survey of Spatial Memory Representations for Efficient Robot Navigation](https://mlsanity.com/paper/2604.16482/) (2026)

## Abstract (arXiv)

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.