Rapid preliminary reconnaissance of Critical Transportation Hubs (CTHs) by UAV swarms is vital during post-disaster rescue operations. However, limited camera fields of view, large heading variances, and GNSS multipath errors near massive steel-concrete structures complicate multi-view cooperative perception. This paper introduces a discrete, vision-based cooperative perception framework utilizing a decentralized anchor-wingman architecture. The pipeline integrates a Prob-IoU-optimized YOLO26m-OBB detector to extract oriented infrastructure footprints. To handle severe rotational discrepancies without IMU priors, a global scene registration cascade—combining SuperPoint and an Optimal Transport-driven LightGlue—is employed to establish robust geometric correspondences. Furthermore, a Projected Polygon Intersection over Union (Proj-IoU) mechanism, coupled with an RMSE-weighted spatial fusion strategy, dynamically associates and deduplicates overlapping targets across distributed views. Experimental results indicate that the framework achieves a low pixel-level RMSE of 2.12 pixels on the source domain and maintains a highly stable 2.36 pixels during zero-shot cross-domain testing (SUES-200 dataset), successfully resolving extreme heading variances up to 270°. The Proj-IoU mechanism resolves multi-source redundancies—collapsing overlapping projections by over 50%—bounding the localization error to approximately 1.06 m. Operating at 6.7 FPS on edge hardware via low-bandwidth tensor transmission, this system provides a rigorous geometric foundation for autonomous swarms, enabling downstream collision-free trajectory planning and Multi-Target Task Allocation (MTTA) in GNSS-denied environments.
Zhi Liu, Yong Xian, Shaopeng Li et al.· Drones· 0 citations
A UAV operating without GNSS faces unbounded inertial drift. A layered navigation architecture is evaluated in which terrain contour matching (TERCOM) provides periodic position corrections and satellite-image scene matching adds a condition-dependent precision layer. The architecture is examined through a single-trajectory simulation over a 1° × 1° ASTER GDEM V2 tile (N31E081, Tibetan Plateau, 4555–6468 m elevation, 16.1 mean slope) representing a one-hour flight (127 km, 35.2 m/s). The simulation models GNSS loss with idealised sensor behaviour: IMU error is described by a Gauss–Markov model without temperature dependence, and the radar altimeter is represented with additive Gaussian noise. Under these conditions, TERCOM reduced RMS position error from 1467 m to 317 m (78.4% reduction); with ideal noise-free scene-matching registration added, RMS further decreased to 103 m (a best-case estimate). The idealised Cramér–Rao lower bound already incorporates the 5 m radar-altimeter and 20 m DEM noise terms (it is therefore not a noise-free value) at the flight mean slope of 16.1°; averaging this local bound over the full trajectory—where near-flat segments inflate it—gives the tile-averaged CRLB of ≈150 m. The remaining gap between the realised TERCOM RMS (317 m) and this realistic bound is attributed to residual INS drift during profile collection, DEM interpolation error, and low-entropy terrain segments; a quantitative decomposition of these factors is provided in this paper. Results are based on a single noise realisation and a single trajectory; they characterise the specific simulation scenario rather than the architecture’s general performance. The altitude-error decomposition argument—that TERCOM’s sensitivity depends primarily on short-term dynamic altitude drift rather than the accumulated systematic error—is developed specifically for the normalised cross-correlation (NCC) metric and requires mean-centring of the terrain profile for generalisation to other correlation metrics.
