Achieving long-term, continuous, and accurate localization for Unmanned Aerial Vehicles (UAVs) in outdoor GNSS-denied environments where pre-existing reference maps are available is challenging. To this end, this paper proposes a Cross-view Particle Filter Localization (CPFL) framework. Unlike existing particle filter approaches that rely on inertial sensors for state propagation or sparse semantic labels for observation updates, CPFL is a vision-driven solution. This framework introduces specific adaptations into the two core stages of particle filtering: In the motion propagation stage, it achieves visual state transition by calculating a feature-based inter-frame homography mapping to estimate the 2D global relative motion components, eliminating the dependency on inertial priors; in the observation correction stage, a Dual-Granularity Adaptive Gating (DGAG) cross-view network is designed to mitigate perceptual aliasing and generate discriminative absolute position weights for the particles. By fusing these two stages through a filter mechanism, the framework transforms unbounded cumulative drift into bounded absolute localization errors. Furthermore, addressing the measurement deficiencies of traditional single-frame metrics, this paper also proposes a Trajectory Continuity Index (TCI@d) tailored for continuous localization tasks. Experiments on the real-world MAFS dataset confirm that this framework achieves a mean localization error of 5.28 m and a localization success rate of 89.7% under a 10-m threshold. Compared with mainstream vision-only algorithms and IMU-fusion baselines, this framework demonstrates lower mean errors and improved trajectory continuity, validating its effectiveness for long-term robustness.

Simultaneous Localization and Mapping (SLAM) is a fundamental methodology that underpins autonomous navigation in robotic systems. Conventional approaches perform well in static environments but rely on the assumption of environmental rigidity, which leads to significant accuracy degradation in dynamic environments. To address this challenge, this study presents RTS-SLAM, a real-time semantic visual SLAM system designed for dynamic environments. Based on the ORB-SLAM2 framework, a multi-layer, constraint-driven dynamic feature-rejection strategy is introduced. The proposed approach first removes dynamic features by combining semantic information with geometric constraints. Subsequently, residual dynamic points are eliminated via trajectory-consistency constraint analysis, thereby effectively improving localization accuracy. Furthermore, a dense mapping strategy featuring global sparsification and critical region refinement is proposed. By reducing redundancy in the dense point cloud, the method decreases memory usage while preserving important object geometries. Experimental evaluations on the TUM RGB-D and Bonn datasets indicate that RTS-SLAM reduces the average absolute trajectory error by more than 95% compared with ORB-SLAM2 in dynamic environments. Meanwhile, the system maintains real-time performance and achieves high localization accuracy in dynamic environments.