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arXiv:2611.06635v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV)-based collaborative perception enables multiple agents to share complementary perceptual information, overcoming limitati...
arXiv:2607. 00191v1 Announce Type: cross Abstract: Collaborative-perception enables multi-robot systems to enhance situational awareness by sharing perceptual information.
CoCam4D is a Bayesian framework for camera-only autonomous driving that models geometric uncertainty in collaborative perception. It employs a VGGT-based network to produce 3D Gaussian scene representations with uncertainty estimates, allowing multiple vehicles to fuse observations without LiDAR. The system introduces Dynamic Object Primitives, a 35‑byte format for efficient C‑V2X communication, and shows significant performance gains over recent vision-only methods on OPV2V+ and DAIR‑V2X‑C benchmarks.
arXiv:2608. 09564v1 Announce Type: cross Abstract: UAV vision-language navigation (UAV-VLN) focuses on enabling an aerial agent to follow natural-language instructions in open 3D environments from egocentric visual observations.
arXiv:2606. 09919v1 Announce Type: cross Abstract: Perceptual uncertainty is a central challenge for heterogeneous robot teams operating in unstructured outdoor environments, where no single viewpoint affords reliable scene understanding.
Aerial image-goal navigation requires an unmanned aerial vehicle (UAV) to reach a target location specified by a goal image. Existing world-model-based methods rank candidate trajectories using predicted futures, but typically rely on only one or a few point predictions, which is inadequate for large-scale outdoor environments with substantial future-state uncertainty.