SWA-PF: Semantic-Weighted Adaptive Particle Filter for Memory-Efficient 4-DoF UAV Localization in GNSS-Denied Environments
Vision-based Unmanned Aerial Vehicle (UAV) localization systems have been extensively investigated for Global Navigation Satellite System (GNSS)-denied environments. However, existing retrieval-based approaches face limitations in dataset availability and persistent challenges including suboptimal real-time performance, environmental sensitivity, and limited generalization capability, particularly in dynamic or temporally varying environments. To overcome these limitations, we present a large-scale Multi-Altitude Flight Segments dataset (MAFS) for variable altitude scenarios and propose a novel Semantic-Weighted Adaptive Particle Filter (SWA-PF) method. This approach integrates robust semantic features from both UAV-captured images and satellite imagery through two key innovations: a semantic weighting mechanism and an optimized particle filtering architecture. Evaluated using our dataset, the proposed method achieves 10x computational efficiency gain over feature extraction methods, maintains global positioning errors below 10 meters, and enables rapid 4 degree of freedom (4-DoF) pose estimation within seconds using accessible low-resolution satellite maps. Code and dataset will be available at https://github.com/YuanJiayuuu/SWA-PF.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencyPose EstimationSimilar Papers 제목 키워드 기반
Learning to track on-the-fly using a particle filter with annealed- weighted QPSO modeled after a singular Dirac delta potential
This paper proposes an evolutionary Particle Filter with a memory guided proposal step size update and an improved, fully-connected Quantum-behaved Particle Swarm Optimization (QPSO) resampling scheme for visual tracking…
Visual TrackingAn overview of differentiable particle filters for data-adaptive sequential Bayesian inference
By approximating posterior distributions with weighted samples, particle filters (PFs) provide an efficient mechanism for solving non-linear sequential state estimation problems. While the effectiveness of particle filte…
Bayesian InferenceSequential Bayesian InferenceState EstimationProgressive Bayesian Particle Flows based on Optimal Transport Map Sequences
We propose a method for optimal Bayesian filtering with deterministic particles. In order to avoid particle degeneration, the filter step is not performed at once. Instead, the particles progressively flow from prior to …
Density EstimationParticle-filtering approaches for nonlinear Bayesian decoding of neuronal spike trains
The number of neurons that can be simultaneously recorded doubles every seven years. This ever increasing number of recorded neurons opens up the possibility to address new questions and extract higher dimensional stimul…
Point ProcessesParticle Flow Gaussian Particle Filter
State estimation in non-linear models is performed by tracking the posterior distribution recursively. A plethora of algorithms have been proposed for this task. Among them, the Gaussian particle filter uses a weighted s…
State Estimation