Safe and Efficient Navigation in Extreme Environments using Semantic Belief Graphs

Understand

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.