# Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation

arXiv: https://arxiv.org/abs/2101.01831 · HTML: https://mlsanity.com/paper/2101.01831/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2021.

## Built on by (most cited first, 7 of 7)

- [EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy](https://mlsanity.com/paper/2311.06234/) (2023)
- [Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map](https://mlsanity.com/paper/2203.13429/) (2022)
- [FrontierNet: Learning Visual Cues to Explore](https://mlsanity.com/paper/2501.04597/) (2025)
- [On the Overconfidence Problem in Semantic 3D Mapping](https://mlsanity.com/paper/2311.10018/) (2023)
- [TrackletMapper: Ground Surface Segmentation and Mapping from Traffic Participant Trajectories](https://mlsanity.com/paper/2209.05247/) (2022)
- [Epistemic Prediction and Planning with Implicit Coordination for Multi-Robot Teams in Communication Restricted Environments](https://mlsanity.com/paper/2302.10393/) (2023)
- [Belief Consistency Between Foundation-Model Evidence and Geometric Perception in Persistent Robotic Maps](https://mlsanity.com/paper/2606.00318/) (2026)

## Abstract (arXiv)

Many robot applications call for autonomous exploration and mapping of unknown and unstructured environments. Information-based exploration techniques, such as Cauchy-Schwarz quadratic mutual information (CSQMI) and fast Shannon mutual information (FSMI), have successfully achieved active binary occupancy mapping with range measurements. However, as we envision robots performing complex tasks specified with semantically meaningful objects, it is necessary to capture semantic categories in the measurements, map representation, and exploration objective. This work develops a Bayesian multi-class mapping algorithm utilizing range-category measurements. We derive a closed-form efficiently computable lower bound for the Shannon mutual information between the multi-class map and the measurements. The bound allows rapid evaluation of many potential robot trajectories for autonomous exploration and mapping. We compare our method against frontier-based and FSMI exploration and apply it in a 3-D photo-realistic simulation environment.