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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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.
DynFly adds a B-spline and flow-matching trajectory layer with UAV-specific dynamic losses to existing UAV-VLN systems, yielding 4.69 NDTW and 4.51 m NE gains on the OpenUAV unseen split.
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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FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation
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.
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DynFly: Dynamic-Aware Continuous Trajectory Generation for UAV Vision-Language Navigation in Urban Environments
DynFly adds a B-spline and flow-matching trajectory layer with UAV-specific dynamic losses to existing UAV-VLN systems, yielding 4.69 NDTW and 4.51 m NE gains on the OpenUAV unseen split.