Unmanned aerial vehicles (UAVs) have become essential platforms for remote sensing in challenging environments such as high-dynamic-range (HDR) scenes and low-texture areas. However, conventional frame-based visual–inertial simultaneous localization and mapping (SLAM) systems often suffer from motion blur and overexposure during high-speed UAV flight, leading to state estimation failure. To address numerical instability in marginalization, weak scene adaptability, and insufficient outlier suppression in event-based stereo visual–inertial SLAM systems for aerial applications, we propose HESVI, a hybrid marginalization and adaptive heterogeneous kernel state estimation method for UAV remote sensing. Our method first establishes a focal-length-driven cross-modal inverse depth consistency constraint to couple image and event inverse depths, providing high-quality priors for optimization. Such lightweight prior generation is designed with the limited onboard computing resources of UAV platforms in mind. A hybrid marginalization strategy is then introduced, employing block-parallel tall–skinny QR (TSQR) acceleration based on Householder reflections alongside dynamic Tikhonov regularization and first-estimates Jacobian (FEJ) linearization to balance computational efficiency and numerical stability. Furthermore, an adaptive heterogeneous Cauchy kernel maps differentiated thresholds to image and event features according to their average effective tracking lengths, enabling dynamic outlier suppression. Experiments on the VECtor, MVSEC, and HKU datasets demonstrate that HESVI achieves the best absolute trajectory error (ATE) on the vast majority of the evaluated sequences, with average ATE reductions of 47.2%, 41.2%, and 26.8% over PL-EVIO, ESIO, and ESVIO, where each average is computed only over the sequences on which the corresponding baseline runs successfully. The method also exhibits excellent performance in complex remote-sensing scenarios and generalization tests. HESVI effectively enhances the numerical stability, scene adaptability, and localization accuracy of event-based stereo visual–inertial SLAM systems in challenging UAV remote-sensing environments.

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.