GNSS Feature Maps – Robust Lane-level Accurate GNSS Navigation in Urban Trenches

Abstract

The demand for high accuracy and high integrity positioning using the Global Navigation Satellite System (GNSS) sensor is on the rise, as GNSS is the only observation system capable of providing absolute positioning information. However, all GNSS positioning strategies are sensitive to the operating environment, posing a substantial challenge in meeting the localisation requirements of autonomous vehicles, particularly in dense urban areas.

The primary source of error for GNSS-based vehicle positioning in these areas is the reception of multipath signals – combinations of direct and reflected signals – and NLOS (Non-Line-of-Sight) signals, which are only reflected signals that reach the antenna. These signals can cause significant inaccuracies in vehicle position estimation regardless of the GNSS positioning technique used.

In this seminar, an innovative strategy is proposed to improve GNSS-based navigation in urban trenches, building upon existing multipath mitigation strategies for single, static stations (i.e., utilising the ground-track repeatability of ranging errors). The goal is to generate a GNSS Feature Map tailored for automotive applications, which consists of pseudorange residual information for all satellite positions in a regular grid along a selected trajectory. This map information is further incorporated into an extended Kalman filter (EKF) framework for GNSS RTK (Real-time Kinematic) positioning, allowing the adaption of robust estimation techniques.

The evaluation and validation of these strategies are carried out based on simulation studies and automotive experiments, located in medium and deep urban trenches. The impact of the GNSS Feature Map information is assessed by means of typical GNSS performance parameters, such as accuracy, integrity and ambiguity resolution.

Speaker

Dr Fabian Ruwisch received his BSc and MSc degree in Geodesy and Geoinformatics from Leibniz University Hannover (LUH) in 2016 and 2019, respectively. He received his PhD in the positioning and navigation group of the Institut für Erdmessung at LUH in 2025. His research interests include GNSS RTK positioning in urban environments, multipath modeling and mitigation strategies as well as robust estimation.