Accurate Global Trajectory Alignment using Poles and Road Markings

TL;DR

This paper introduces a novel geo-referencing method that aligns vehicle trajectories to aerial imagery using poles and road markings, improving map accuracy despite GNSS limitations in urban environments.

Contribution

The proposed approach combines feature extraction, RANSAC-based matching, and geometric optimization to enhance trajectory alignment to aerial imagery, addressing GNSS inaccuracies.

Findings

Accurately aligned trajectories to aerial imagery in urban environments.

Robust feature matching using RANSAC in a sliding window.

Improved geo-referencing accuracy demonstrated in Karlsruhe, Germany.

Abstract

Currently, digital maps are indispensable for automated driving. However, due to the low precision and reliability of GNSS particularly in urban areas, fusing trajectories of independent recording sessions and different regions is a challenging task. To bypass the flaws from direct incorporation of GNSS measurements for geo-referencing, the usage of aerial imagery seems promising. Furthermore, more accurate geo-referencing improves the global map accuracy and allows to estimate the sensor calibration error. In this paper, we present a novel geo-referencing approach to align trajectories to aerial imagery using poles and road markings. To match extracted features from sensor observations to aerial imagery landmarks robustly, a RANSAC-based matching approach is applied in a sliding window. For that, we assume that the trajectories are roughly referenced to the imagery which can be achieved by rough GNSS measurements from a low-cost GNSS receiver. Finally, we align the initial trajectories precisely to the aerial imagery by minimizing a geometric cost function comprising all determined matches. Evaluations performed on data recorded in Karlsruhe, Germany show that our algorithm yields trajectories which are accurately referenced to the used aerial imagery.