Name
Abstract | ||
|---|---|---|
In this paper, we present a novel geo-referencing approach to align trajectories to an aerial imagery using pole and road marking features. Currently, digital maps are indispensable for automated driving. However, due to the low precision and reliability of Global navigation satellite systems (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 an aerial imagery seems promising. Furthermore, an accurate geo-referencing improves the global map accuracy and allows to estimate the sensor calibration error. To match extracted features from sensor observations to landmarks extracted from an aerial imagery robustly, a matching approach using RANSAC 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 show that our algorithm yields trajectories which are accurately referenced to the used aerial imagery. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IVS.2019.8814054 | 2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19) |
Field | DocType | Volume |
Sliding window protocol,Global Map,Pattern recognition,Digital mapping,RANSAC,Computer science,Artificial intelligence,GNSS applications,Calibration Error,Aerial imagery,Trajectory | Journal | abs/1903.10205 |
ISSN | Citations | PageRank |
1931-0587 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haohao Hu | 1 | 0 | 0.34 |
| Marc Sons | 2 | 0 | 1.35 |
| Christoph Stiller | 3 | 2189 | 153.23 |