NeuroKalman mitigates state drift in vision-language UAV navigation by using memory-augmented Kalman filtering where attention retrieves historical anchors to correct predictions without gradient updates.
Flightgpt: Towards generalizable and interpretable uav vision-and-language navigation with vision-language models.arXiv preprint arXiv:2505.12835
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DynFly bridges high-level UAV navigation reasoning to continuous motion via B-spline trajectory generation with flow matching and UAV-specific dynamic supervision, yielding metric gains on the OpenUAV benchmark.
Introduces UAV-VLN-FOV task and 3DG-VLN framework for precise target-visible UAV navigation, reporting 13.82% success rate gain on a new 2,717-trajectory benchmark with code released.
FineCog-Nav uses fine-grained cognitive modules driven by foundation models to outperform zero-shot baselines in UAV navigation and introduces the AerialVLN-Fine benchmark with refined instructions.
citing papers explorer
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Mitigating Error Accumulation in Continuous Navigation via Memory-Augmented Kalman Filtering
NeuroKalman mitigates state drift in vision-language UAV navigation by using memory-augmented Kalman filtering where attention retrieves historical anchors to correct predictions without gradient updates.
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DynFly: Dynamic-Aware Continuous Trajectory Generation for UAV Vision-Language Navigation in Urban Environments
DynFly bridges high-level UAV navigation reasoning to continuous motion via B-spline trajectory generation with flow matching and UAV-specific dynamic supervision, yielding metric gains on the OpenUAV benchmark.