# Safe and Efficient Navigation in Extreme Environments using Semantic Belief Graphs

arXiv: https://arxiv.org/abs/2304.00645 · HTML: https://mlsanity.com/paper/2304.00645/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2023.

## Built on by (most cited first, 3 of 3)

- [SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments](https://mlsanity.com/paper/2410.03035/) (2024)
- [Belief Scene Graphs: Expanding Partial Scenes with Objects through Computation of Expectation](https://mlsanity.com/paper/2402.03840/) (2024)
- [Estimating Commonsense Scene Composition on Belief Scene Graphs](https://mlsanity.com/paper/2505.02405/) (2025)

## Abstract (arXiv)

To achieve autonomy in unknown and unstructured environments, we propose a method for semantic-based planning under perceptual uncertainty. This capability is crucial for safe and efficient robot navigation in environment with mobility-stressing elements that require terrain-specific locomotion policies. We propose the Semantic Belief Graph (SBG), a geometric- and semantic-based representation of a robot's probabilistic roadmap in the environment. The SBG nodes comprise of the robot geometric state and the semantic-knowledge of the terrains in the environment. The SBG edges represent local semantic-based controllers that drive the robot between the nodes or invoke an information gathering action to reduce semantic belief uncertainty. We formulate a semantic-based planning problem on SBG that produces a policy for the robot to safely navigate to the target location with minimal traversal time. We analyze our method in simulation and present real-world results with a legged robotic platform navigating multi-level outdoor environments.