A research paper titled Vision-Enhanced Low-Cost Localization in Crowdsourced Maps with TransSec researchers Axel Koppert (OHB Digital Solutions GmbH) and Gorka Velez (Vicomtech) has been published on IEEE Intelligent Transportation Systems Magazine (Impact Factor 3.363). This well-reputed magazine publishes peer-reviewed articles that provide innovative research ideas and application results, report significant application case studies, and raise awareness of pressing research and application challenges in all areas of intelligent transportation systems.
Below is the abstract of the paper:
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.