Vision-Enhanced Low-Cost Localization In Crowdsourced Maps

Benedict Flade, Axel Koppert, Gorka Velez, Anweshan Das, David Betaille, Gijs Dubbelman, Oihana Otaegui, Julian Eggert

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review


The lane-level localization of vehicles with low-cost sensors is a challenging task. In situations in which Global Navigation Satellite Systems (GNSSs) suffer from weak observation geometry or from the influence of reflected signals, the fusion of heterogeneous information presents a suitable approach for improving the localization accuracy. We propose a solution based on a monocular front-facing camera, a low-cost inertial measurement unit (IMU), and a single-frequency GNSS receiver. The sensor data fusion is implemented as a tightly coupled Kalman filter that corrects the IMU-based trajectory with GNSS observations while employing European Geostationary Overlay Service correction data. Further, we consider vision-based complementary data that serve as an additional source of information. In contrast to other approaches, the camera is not used to infer the motion of the vehicle, but rather for directly correcting the localization results under the usage of map information. More specifically, the so-called camera-to-map alignment is done by comparing virtual 3D views (candidates) created from projected map data with lane geometry features that are extracted from the camera image. One strength of the proposed solution is its compatibility with state-of-the-art map data, which are publicly available from different sources. We validate the approach on real-world data recorded in The Netherlands and show that it presents a promising and cost-efficient means to support future advanced driver assistance systems.
Originele taal-2Engels
Pagina's (van-tot)70-80
Aantal pagina's11
TijdschriftIEEE Intelligent Transportation Systems Magazine
Nummer van het tijdschrift3
StatusGepubliceerd - 1 sep 2020

Vingerafdruk Duik in de onderzoeksthema's van 'Vision-Enhanced Low-Cost Localization In Crowdsourced Maps'. Samen vormen ze een unieke vingerafdruk.

Citeer dit