project . 2021 - 2023 . On going


Galileo/GNSS-based Autonomous Mobile Mapping System
Open Access mandate for Publications
European Commission
Funder: European CommissionProject code: 101004255 Call for proposal: H2020-SPACE-EGNSS-2020
Funded under: H2020 | IA Overall Budget: 1,929,290 EURFunder Contribution: 1,383,760 EUR
Status: On going
01 Jul 2021 (Started) 31 Dec 2023 (Ending)
Open Access mandate
Research data: No

In GAMMS we will develop an autonomous terrestrial mobile mapping system; i.e. a mobile mapping system (MMS) robot for geodata acquisition and an AI-based highly automated mapping software. In contrast to today’s manned MMS whose cost is dominated by 2- to 3-people crews, we envision fleets of low-cost, autonomous, electrically-powered land vehicles, carrying mobile mapping systems (MMS) and collecting geodata in a massive, continuous way. Although we will develop generalpurpose geodata acquisition and processing techniques, in GAMMS we focus on the rapidly growing market of the High Definition (HD) maps for the autonomous vehicles (AVs), a.k.a. self-driving cars. Because of the enormous task of mapping the world roads for AVs we will develop highly automated software to produce HD maps from the MMS remote sensing data. Because of the safety requirements of AVs, we will also develop map certification methods and quasi real-time, online techniques to continuously update the HD maps. The building blocks of GAMMS are: an electrically-powered AV, a MMS, a GNSS/Galileo receiver, multi-sensor trajectory determination software, multispectral laser scanners, vehicle dynamic models, automated mapping software and mission risk analysis methods. A keystone of GAMMS –which encompasses the extension of the Galileo receiver and the development of ultra-safe, ubiquitous navigation methods at the 5 cm error level– is the use of Galileo features (e.g. E5 AltBOC signal) and new services: navigation message authentication (NMA), high-accuracy serive (HAS) and signal authentication. Galileo and our trajectory determination methods enable the GAMMS concept. Our market value proposition is the production of high-accuracy high-reliable maps at a fraction of today’s cost. In a first fielding of the AMMS technology we will focus on the skyrocketing market of HD maps and, for this particular application, our value proposition includes the quasi real-time, continuous online

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