Dun Dai | Searcharxiv

arXiv2026

From Multi-Fisheye Sensing to Panoramic Perception: A Parallax-Aware Onboard Platform for Ultra-Low-Altitude UAVs

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. Ours adds content-adaptive seam search and a validation-gated residual mesh and is evaluated under a sensor-rate deployment configuration. Evaluation uses more than 50,000 four-view groups from 18 field sequences. Relative to Fixed Depth, Ours reduces far-field P90 feature misalignment by 41.6% and achieves the lowest aggregate geometric errors across held-out sites. At a paced 20 Hz input rate, Ours sustains 19.99 frames/s at 13.29 W 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. Source code is available at https://github.com/DUNDAI1998/parallax-aware-uav-panorama.git.

cs.RO↗

arXiv2025

Correspondence-Free Pose Estimation with Patterns: A Unified Approach for Multi-Dimensional Vision

6D pose estimation is a central problem in robot vision. Compared with pose estimation based on point correspondences or its robust versions, correspondence-free methods are often more flexible. However, existing correspondence-free methods often rely on feature representation alignment or end-to-end regression. For such a purpose, a new correspondence-free pose estimation method and its practical algorithms are proposed, whose key idea is the elimination of unknowns by process of addition to separate the pose estimation from correspondence. By taking the considered point sets as patterns, feature functions used to describe these patterns are introduced to establish a sufficient number of equations for optimization. The proposed method is applicable to nonlinear transformations such as perspective projection and can cover various pose estimations from 3D-to-3D points, 3D-to-2D points, and 2D-to-2D points. Experimental results on both simulation and actual data are presented to demonstrate the effectiveness of the proposed method.

cs.CV↗