State estimation plays a vital role in UAV navigation and control. With the continuous decrease in sensor cost and size, UAVs equipped with multiple LiDARs, Inertial Measurement Units (IMUs), and cameras have attracted increasing attention. Such systems can acquire rich environmental and motion information from multiple perspectives, thereby enabling more precise navigation and mapping in complex environments. However, efficiently utilizing multi-sensor data for state estimation remains challenging. There is a complex coupling relationship between IMUs’ bias and UAV state. To address these challenges, this paper proposes an efficient and accurate UAV state estimation method tailored for multi-LiDAR–IMU–camera systems. Specifically, we first construct an efficient distributed state estimation model. It decomposes the multi-LiDAR–IMU–camera system into a series of single LiDAR–IMU–camera subsystems, reformulating the complex coupling problem as an efficient distributed state estimation problem. Then, we derive an accurate feedback function to constrain and optimize the UAV state using estimated subsystem states, thus enhancing overall estimation accuracy. Based on this model, we design an efficient distributed state estimation algorithm with multi-LiDAR-IMU-Camerafusion, termed DLIC. DLIC achieves robust multi-sensor data fusion via shared feature maps, effectively improving both estimation robustness and accuracy. In addition, we design an accelerated image-to-point cloud registration module (A-I2P) to provide reliable visual measurements, further boosting state estimation efficiency. Extensive experiments are conducted on 18 real-world indoor and outdoor scenarios from the public NTU VIRAL dataset. The results demonstrate that DLIC consistently outperforms existing multi-sensor methods across key evaluation metrics, including RMSE, MAE, SD, and SSE. More importantly, our method runs in real time on a resource-constrained embedded device equipped with only an 8-core CPU, while maintaining low memory consumption.

Event cameras provide microsecond-level temporal resolution, low latency, and high dynamic range, enabling robust perception under fast motion and challenging lighting conditions. Nevertheless, event streams are susceptible to background activity, thermal noise, and hot pixels. Their sparse and irregular patterns can corrupt event structures and degrade downstream tasks. We propose MSF, a multi-level spatiotemporal filtering framework that couples motion-compensated aggregation with neighborhood-level verification. In each temporal window, MSF estimates a constant 2D optical flow by maximizing a robust, density-normalized contrast objective on the image of warped events (IWE). We further incorporate polarity–gradient decorrelation to suppress mixed-polarity noise and an explicit peak-suppression regularizer to avoid hot-pixel-induced degeneracy. The motion parameters are optimized via coarse grid initialization followed by gradient-ascent refinement. Based on the estimated motion, MSF performs hierarchical event selection: central events are extracted from high-confidence aggregated regions, local events are recovered through joint spatial–temporal–directional–polarity consistency, and weak border events are identified using a density-normalized probabilistic support model that rewards support from reliable structures while penalizing self-clustering. Experiments on four public benchmarks (DVSNOISE20, DVSMOTION20, DVSCLEAN, and E-MLB) show that MSF consistently improves the Event Structural Ratio (ESR) and outperforms representative baselines across diverse motion regimes and severe low-light noise.