MEVFusion-LD: Adaptive Scoring and Match-Quality-Weighted Loop-Closure Detection Based on MEMS Inertial and Event Vision Fusion

Event cameras and low-cost microelectromechanical systems (MEMS) inertial measurement units (IMUs) are widely used in event-based visual–inertial odometry (EVIO). However, the bias instability and noise of MEMS gyroscopes accelerate drift accumulation, making loop-closure detection essential for global consistency. Existing EVIO systems largely inherit loop-detection pipelines from conventional visual–inertial odometry, whose fixed score thresholds and raw inlier counts degrade when event-frame quality fluctuates during high-speed motion or under high-dynamic-range illumination. This paper proposes MEVFusion-LD, which fuses MEMS inertial and event-vision measurements using adaptive scoring and match-quality weighting. First, an adaptive scoring threshold (AST) mechanism updates the candidate-entry threshold online from the first- and second-order statistics of query scores, with bilateral clamping. Second, temporal-consistency voting accumulates retrieval votes within a sliding cache window to suppress sporadic false candidates. Third, match-quality-weighted verification (MQWV) replaces the raw inlier count with a quality-weighted inlier ratio for geometric confirmation, followed by 4-DoF pose-graph optimization for online drift correction. Experiments on the VECtor and MVSEC datasets, both collected using MEMS-grade inertial sensors, show that MEVFusion-LD reduces the mean absolute trajectory error by 29.8%, 41.1%, and 50.0% relative to ESVIO, ESIO, and PL-EVIO, respectively, achieves the best accuracy on 9 of 11 sequences, and reduces ATE by 52.8% on average through the loop module alone.