A Hybrid Position Estimation Framework Based on GNSS and Visual Sensor Fusion

Sara Baldoni, Federica Battisti, Michele Brizzi, Alessandro Neri

Abstract: Abstract—The objective of this work is to evaluate the feasibility of the integration of visual sensors in the framework of GNSSbased vehicle localization systems. Undoubtedly GNSS plays a key role in vehicle localization. However, the impact of GNSS faults (e.g. satellite and constellation failure), signal degradation (e.g. ionospheric scintillations, multipath) and external threats (e.g. jamming and spoofing) need to be taken into account. To address this issue, we propose a three-level localization framework based on the use of additional visual sensors. Thanks to the visual input, the inaccurate GNSS measurements are refined by tracking georeferenced landmarks to obtain the vehicle absolute position. Moreover, the same sensors can be used to obtain the relative position and motion of the vehicle. Every time a new group of landmarks is detected, it is possible to repeat the absolute positioning process to cancel the drift accumulated by the relative localization procedure. This work represents the first step towards the realization of the proposed localization technique. Initial testing to evaluate its suitability has been carried out and is reported hereafter.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 979 - 986
Cite this article: Baldoni, Sara, Battisti, Federica, Brizzi, Michele, Neri, Alessandro, "A Hybrid Position Estimation Framework Based on GNSS and Visual Sensor Fusion," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 979-986.
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