  This paper addresses the problem of autonomous robotic inspection in complex
and unknown environments. This capability is crucial for efficient and precise
inspections in various real-world scenarios, even when faced with perceptual
uncertainty and lack of prior knowledge of the environment. Existing methods
for real-world autonomous inspections typically rely on predefined targets and
waypoints and often fail to adapt to dynamic or unknown settings. In this work,
we introduce the Semantic Belief Behavior Graph (SB2G) framework as a novel
approach to semantic-aware autonomous robot inspection. SB2G generates a
control policy for the robot, featuring behavior nodes that encapsulate various
semantic-based policies designed for inspecting different classes of objects.
We design an active semantic search behavior to guide the robot in locating
objects for inspection while reducing semantic information uncertainty. The
edges in the SB2G encode transitions between these behaviors. We validate our
approach through simulation and real-world urban inspections using a legged
robotic platform. Our results show that SB2G enables a more efficient
inspection policy, exhibiting performance comparable to human-operated
inspections.
