Reliability-Gated Stereo Vision and 2.5D Sensor Fusion for Active Robotic Observation in Occluded Manufacturing...

Active robotic target observation in occluded manufacturing workspaces is difficult when unreliable stereo depth, missing obstacle-height information, and infeasible viewpoint commands propagate through the perception-mapping-planning loop. This study develops a reliability-gated stereo vision and 2.5D fusion framework for active target observation with an eye-in-hand stereo system. The framework screens low-quality stereo observations before fusion, verifies whether accepted observations produce effective map updates, and maintains a 2.5D occupancy-height representation for height-aware viewpoint generation. Using this representation, constrained semantic-geometric ranking selects next-best-view (NBV) candidates, while execution feedback, failed-region avoidance, and anchor recovery maintain closed-loop continuity. Closed-loop simulations are conducted in occluded robotic workcell scenarios with different difficulty levels. The method achieves 100% success in all Easy and Medium scenes. In Medium2, success rates are 80% for the 2D baseline and 100% for the 2.5D representation; the corresponding mean search steps among successful trials are 6.12 ± 0.64 and 3.40 ± 0.70, respectively. Real-scene stereo front-end validation supports depth recovery, quality-gated observation screening, initial 2.5D map fusion, and first-NBV generation under occlusion. These results indicate that reliability-gated stereo fusion and height-aware viewpoint planning can improve active robotic observation in occluded manufacturing workspaces.