Doorway-Aware Frontier Scheduling for Indoor Mobile Robots Using RGB-D Geometric Screening and Temporal Semantic Anchors
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Abstract
Indoor mobile robots must select navigation goals while operating with incomplete maps, but raw visual semantic detections can be too unstable to influence planning directly. This paper presents a doorway-aware frontier-scheduling framework that combines RGB-D geometric screening, temporal semantic-anchor maintenance, an incremental semantic-support layer, and anchor-dependent frontier-utility reweighting. Doorway candidates enter the planning loop only after depth-validity, local-plane, and geometric-consistency checks; accepted observations are then fused into persistent anchors whose confidence decays after missed updates. In ten paired same-condition simulations, the proposed configuration increased the mean semantic-support activation ratio from 5.11 × 10−4 to 1.447 × 10−3 and was higher in nine of the ten paired runs (two-sided exact sign test, p = 0.0215). The mean number of completed navigation goals increased from 0.10 to 2.50 per run, and all episodes terminated normally under the fixed evaluation protocol. Single-run demonstrations in two auxiliary layouts confirmed closed-loop executability but were not treated as statistical generalization evidence. The results support the feasibility of using geometrically screened, temporally maintained doorway cues to modify frontier scheduling; direct traversable-area coverage, detector accuracy, runtime, and real-robot performance remain outside the present evidence.
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