Multi-phase vision-based navigation and inspection for legged robots with online goal refinement and vision-only halting

Vision-based navigation is important for autonomous legged robots, enabling semantic target search and inspection without dense maps or specialized sensors. However,existing goal-conditioned navigation methods often rely on pre-curated goal images, lack reliable stopping mechanisms, and are sensitive to detector noise and gait-induced camera perturbations. To address these limitations, we pro-pose a multi-phase vision-based navigation and inspection framework for quadrupeds using only monocular RGB in-put. First, we introduce online detection-driven goal refinement, which extracts target crops from the robot’s live camera stream and progressively updates the goal representation as the robot approaches the target, reducing dependence on pre-collected goal images and improving robustness to distant or low-quality detections. Second, we design a phase-factorized policy structure that decomposes the task into long-range approach and close-range orbital inspection, using two specialized checkpoints of the same navigation backbone to better handle the different control requirements of each phase. Third, we develop a vision-only halting and target-lock mechanism that uses the bbox-to-frame-area ratio with temporal smoothing to trigger stable depth-free stopping, while an image-based visual servoing loop keeps the target centered despite detector jitter and gait-induced camera motion. Experiments on a 12-DOFquadruped across on-axis, 60◦, and 90◦ outdoor scenarios show superior performance, achieving 85–95% end-to-end success with consistent ∼1.0–1.2 m stopping distance. Ablations further validate the contribution of each component,and the framework generalizes to both ViNT and NoMaD backbones without architectural modification.