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Localization in Global Positioning System–Denied Environments Using Infrastructure-Embedded Analog-Digital Information
Journal Article
12-06-04-0029
ISSN: 2574-0741, e-ISSN: 2574-075X
Sector:
Topic:
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.
Language:
English
Abstract:
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.