Vehicle self-localization in GPS-denied zones by multi-band imaging and analysis of prominent scene features

J. Mares; M. Martino; A. Irwin; L. Zhang; O. Furxhi; C. K. Renshaw

doi:10.1117/12.2663660

13 June 2023 Vehicle self-localization in GPS-denied zones by multi-band imaging and analysis of prominent scene features

J. Mares, M. Martino, A. Irwin, L. Zhang, O. Furxhi, C. K. Renshaw

Author Affiliations +

Proceedings Volume 12534, Infrared Technology and Applications XLIX; 125341P (2023) https://doi.org/10.1117/12.2663660
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The ability to reliably and accurately ascertain a vehicle’s position is imperative for military operations as well as civilian and commercial navigation systems. Due to the susceptibility of GPS signals to RF spoofing and jamming, alternative means of vehicle self-localization are garnering substantial interest. Vision-based methods are among the most promising in environments with sufficiently distinguishable features such as towers, high-rise structures, and prominent identifiable topographical features. Here, we present a localization approach exploiting multiple spectral bands to identify key prominent scene features and determine vehicle position relative to those features to calculate a global vehicle position and heading. We employ geometric dead-reckoning using visible and LWIR imagery to quantify positional accuracy that is achievable with these bands. We utilize image recognition algorithms to identify features and map these into useful parameters for position extraction, leveraging geospatial data when possible.

Conference Presentation

Citation Download Citation

J. Mares, M. Martino, A. Irwin, L. Zhang, O. Furxhi, and C. K. Renshaw "Vehicle self-localization in GPS-denied zones by multi-band imaging and analysis of prominent scene features", Proc. SPIE 12534, Infrared Technology and Applications XLIX, 125341P (13 June 2023); https://doi.org/10.1117/12.2663660

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