FKFS Veranstaltungen

2025 Stuttgart International Symposium
on Automotive and Engine Technology

3. - 4. Juli 2025

Session: Autonomous Driving II | | 16:00 - 16:30

Environment-Adaptive Localization based on GNSS, Odometry and LiDAR Systems

Markus Kramer, TU Darmstadt

In the evolving landscape of automated driving systems, the critical role of vehicle localization within the autonomous driving stack is increasingly evident. Traditional reliance on Global Navigation Satellite Systems (GNSS) proves to be inadequate, especially in urban areas where signal obstruction and multipath effects degrade accuracy. Addressing this challenge, this paper details the enhancement of a localization system for autonomous public transport vehicles, focusing on mitigating GNSS errors through the integration of a LiDAR sensor. The approach involves creating a 3D map using the factor graph-based LIO-SAM algorithm based on GNSS, vehicle odometry, IMU and LiDAR data. The algorithm is adapted to the use-case by adding a velocity factor and altitude data from a Digital Terrain model. Based on the map a state estimator is proposed, which combines high-frequency LiDAR odometry based on FAST-LIO with low-frequency absolute multiscale ICP-based LiDAR position estimation. The state estimator is enhanced by a simple yet effective delay compensation method to enable operation at higher velocities. To facilitate its integration into the remaining localization system an error-state Kalman filter is presented, providing adequate measurement covariances for the LiDAR solution. For the fusion of LiDAR- and GNSS-based position estimates the alignment between reported and real measurement uncertainties is crucial. However this requirement is not always met in real-world applications. A key aspect of automated public transport is the fact that the route to be travelled is known in advance. This enables an empirical evaluation of the route sections with regard to their suitability for GNSS and LiDAR localization. On this basis, a geo-based adjustment of the measurement covariance is proposed. To enable smooth transitions between the sub-areas a gaussian kernel is applied on the covariance offsets. The performance of the mapping and localization components is validated with real driving data, demonstrating improved stability and accuracy compared to the GNSS-based localization system.