Document Type
Article
Publication Date
12-19-2025
Abstract
Estimating the position and orientation of a rigid object from an image is critical for situational awareness in robotics and autonomous systems. This study explores relative pose estimation using an ultra-wide fisheye camera for unmanned aircraft inspection vehicles. Ultra-wide fisheye lenses introduce radial distortion and capture features beyond the rectilinear image plane, rendering rectilinear Perspective-n-Point (PnP) algorithms inadequate. Designing a bespoke ultra-wide fisheye localization algorithm requires consideration of both the feature detection method and the pose estimator itself. This study proposes a novel method that combines (1) a fisheye-to-cubemap reprojection, (2) a You Only Look Once (YOLO) convolutional neural network trained for arbitrary airborne perspectives, and (3) an Angle-Agnostic and Multiple-Frame PnP (AMP) pose estimation algorithm. Our pipeline achieves a 97% success rate for valid pose estimates, with a mean absolute translational error of less than 12 cm on real ultra-wide fisheye imagery, outperforming conventional techniques, including OpenCV.
Source Publication
Machine Vision and Applications (ISSN 0932-8092 | eISSN 1432-1769)
Recommended Citation
Raettig, R.M., Nyquist, R.R., Nykl, S.L. et al. Amp: single-shot ultra-wide fisheye-to-cubemap PnP pose estimation. Machine Vision and Applications 37, 18 (2026). https://doi.org/10.1007/s00138-025-01768-8
Included in
Artificial Intelligence and Robotics Commons, Navigation, Guidance, Control and Dynamics Commons
Comments
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Supplementary material: A supplementary file (MP4) is available at the publisher's abstract page for this article, reachable using the DOI link below.
The article was published as a digital article of Machine Vision and Applications in December 2025, ahead of inclusion in volume 37 as cited below.