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Localization in Global Positioning System–Denied Environments Using Infrastructure-Embedded Analog-Digital Information
ISSN: 2574-0741, e-ISSN: 2574-075X
Published May 11, 2023 by SAE International in United States
Citation: Moosavi, S., Weaver, A., and Gopalswamy, S., "Localization in Global Positioning System–Denied Environments Using Infrastructure-Embedded Analog-Digital Information," SAE Intl. J CAV 6(4):2023, https://doi.org/10.4271/12-06-04-0029.
While a majority of transportation and mobility solutions rely on in-vehicle sensors and the availability of the global positioning system (GPS) for absolute localization, alternate paradigms leveraging smart infrastructure have started becoming a viable solution for localization without needing GPS. However, the majority of approaches involving smart infrastructure require a means for wireless communication. In this article, we describe a novel method that can accurately localize the vehicle without using GPS and wireless communication by leveraging embedded digital and analog information on the roadside signage. The embedded information consists of a digital signature which can be used to cross-reference the ground truth (GT) location of the signage, as well as geometric information of the signage. This information is directly leveraged by on-vehicle sensors to generate absolute localization information. Specifically, the smart infrastructure consists of signage that is visible primarily in the infrared (IR) spectrum. A specialized camera that is optimized to read the digital signature extracts the analog information associated with the signage (ground truth and geometry). This is then used by both the camera, as well as a millimeter (mm)-wave radar to produce independent localization information. The camera and radar information are correlated with the signage information using a global nearest neighbor algorithm, followed by fusion with vehicle odometry using an extended Kalman filter (EKF) to generate accurate localization of the vehicle. The EKF is set up to manage asynchronous observations between the camera, radar, and vehicle odometry. The proposed method is implemented to localize a vehicle without the aid of GPS, and the results show consistent localization with the root mean squared (RMS) longitudinal and lateral errors less than 0.46 m and 0.19 m, respectively.