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.

翻译：为实现未知非结构化环境中的自主性，我们提出了一种基于语义的感知不确定性规划方法。该能力对于在包含需要特定地形移动策略的移动性挑战元素的环境中，实现安全高效的机器人导航至关重要。我们提出语义信念图（SBG），这是一种基于几何与语义的机器人环境概率路径图表示。SBG节点包含机器人的几何状态及环境中地形的语义知识，SBG边则代表驱动机器人在节点间移动或触发信息收集行为以减少语义信念不确定性的局部语义控制器。我们在SBG上构建语义规划问题，生成使机器人以最小通行时间安全抵达目标位置的策略。通过仿真分析验证该方法，并展示了腿式机器人在多层室外环境中的实际导航结果。