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

Abstract

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.