Coherence-Aware Transition-Intent Fusion for LTL Planning under Uncertain Semantic Maps

Robots operating in semantic environments often need to satisfy linear temporal logic (LTL) tasks before the semantic map is fully known. Many practical semantic-LTL planners maintain semantic beliefs but execute a single task policy induced by a point semantic interpretation, such as a maximum-a-posteriori (MAP) semantic map, possibly supported by risk checks or active perception. This premature commitment can be brittle when distant semantic observations are weak or correlated. We propose Coherence-Aware Transition-Intent Fusion, an online planner that preserves automaton-relevant semantic uncertainty using a finite top-$K$ intent abstraction. Given a conservative current automaton estimate, the planner propagates plausible semantic assignments through the automaton, converts feasible successor transitions into weighted reach-avoid intents, and fuses their risk-gated Dijkstra progress scores. When competing intents create incoherent motion, a KL-regularized calibration step shifts weight toward intents consistent with recent physical progress. Experiments on random-obstacle and room-like semantic maps show improved satisfaction over single-intent and raw-fusion variants, and over a TOAPP* MAP-plus-active-perception baseline under the same static-view sensor. Ablations attribute the gain primarily to intent-level calibration.