aiqbench analysis
This research presents a specialized onboard platform for ultra-low-altitude UAVs that fuses four fisheye streams into high-fidelity panoramic views using NVIDIA Jetson hardware. The system employs parallax-aware algorithms to significantly reduce geometric errors in feature alignment, enabling robust visual navigation near complex obstacles like buildings and vegetation. By unifying sensor-rate execution with reusable vision interfaces, this work advances the practical deployment of embedded perception architectures for autonomous aerial systems.
From Multi-Fisheye Sensing to Panoramic Perception: A Parallax-Aware Onboard Platform for Ultra-Low-Altitude UAVs
arXiv:2609.02319v1 Announce Type: new Abstract: Ultra-low-altitude unmanned aerial vehicles (UAVs) require surround vision near buildings, vegetation, and other obstacles. We present a parallax-aware onboard platform that converts four synchronized fisheye streams into an open 1280x640 equirectangular panorama (ERP) interface. A purpose-built carbon-fiber airframe integrates the cameras, NVIDIA Jetson Orin NX, a flight controller, and a global navigation satellite system (GNSS) receiver. The formation pipeline selects projection depth per overlap and combines controlled seams and photometric fusion. Its accuracy profile adds content-adaptive seam search and a validation-gated residual mesh, whereas its deployed profile retains margin-gated Per-seam updates for sensor-rate operation. Evaluation uses more than 50,000 four-view groups from 18 field sequences. Relative to Fixed Depth, the accuracy profile reduces far-field P90 feature misalignment by 41.6%; the deployed Per-seam profile achieves the lowest aggregate geometric errors across held-out sites. Under a paced 20 Hz replay, the deployed profile sustains 19.99 frames/s at 13.29W mean module-input power. Eight-sector ERP sampling reaches 90.8% mean daytime visual-place-recognition Recall@5. Together, these results validate an integrated onboard panoramic-perception architecture that unifies parallax-aware formation, sensor-rate embedded execution, and reusable downstream vision interfaces for ultra-low-altitude UAVs. The project has been open-sourced at https://github.com/DUNDAI1998/parallax-aware-uav-panorama.
Abstract:Ultra-low-altitude unmanned aerial vehicles (UAVs) require surround vision near buildings, vegetation, and other obstacles. We present a parallax-aware onboard platform that converts four synchronized fisheye streams into an open 1280x640 equirectangular panorama (ERP) interface. A purpose-built carbon-fiber airframe integrates the cameras, NVIDIA Jetson Orin NX, a flight controller, and a global navigation satellite system (GNSS) receiver. The formation pipeline selects projection depth per overlap and combines controlled seams and photometric fusion. Its accuracy profile adds content-adaptive seam search and a validation-gated residual mesh, whereas its deployed profile retains margin-gated Per-seam updates for sensor-rate operation. Evaluation uses more than 50,000 four-view groups from 18 field sequences. Relative to Fixed Depth, the accuracy profile reduces far-field P90 feature misalignment by 41.6%; the deployed Per-seam profile achieves the lowest aggregate geometric errors across held-out sites. Under a paced 20 Hz replay, the deployed profile sustains 19.99 frames/s at 13.29W mean module-input power. Eight-sector ERP sampling reaches 90.8% mean daytime visual-place-recognition Recall@5. Together, these results validate an integrated onboard panoramic-perception architecture that unifies parallax-aware formation, sensor-rate embedded execution, and reusable downstream vision interfaces for ultra-low-altitude UAVs. The project has been open-sourced at this https URL.
Robotics (cs.RO)
arXiv:2609.02319 [cs.RO]
(or arXiv:2609.02319v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.02319 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 284 words
Related coverage
CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking
arXiv · Sep 9, 2026
AGT-CV: An Aerial-Ground Team Cross-View Dataset for Heterogeneous Robot Teams in Unstructured Environments
arXiv · Oct 5, 2026
FingerViP: Learning Real-World Dexterous Manipulation with Fingertip Visual Perception
arXiv · Sep 25, 2026
EmbPASS: Towards Cross-Embodiment Open Panoramic Segmentation
arXiv · Oct 5, 2026