Name
Abstract | ||
|---|---|---|
The self-localization of a vehicle is still an ongoing and challenging task for the autonomous driving development. At the same time, the correct understanding of the vehicle's surroundings and the creation of a map with the traversed trajectory is essential for driving in complex urban scenarios. This paper proposes a solution to create an enriched global map of the environment while localizing the vehicle within it. We use the Evidential framework based on the Dempster-Shafer theory to create a map able to distinguish between static and dynamic obstacles and to keep the information from the entire traversed path. Additionally, we propose a method that at each current map creation estimates the vehicle's position by a grid matching algorithm based on image registration techniques. The method transforms the grid maps into images using the evidential data to extract relevant information to perform a better localization. The proposed method was tested on the KITTI dataset and shows that the solution is able to reduce the drift and improve the localization compared to other methods using similar configurations. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICARCV.2018.8581065 | 2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV) |
Field | DocType | ISSN |
Computer vision,Global Map,Computer science,Control engineering,Artificial intelligence,Blossom algorithm,Grid,Trajectory,Image registration | Conference | 2474-2953 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michelle Valente | 1 | 0 | 1.69 |
| Cyril Joly | 2 | 3 | 4.46 |
| Arnaud de La Fortelle | 3 | 264 | 31.52 |