Name
Abstract | ||
|---|---|---|
Localization is an essential capability of mobile vehicles such as robots or autonomous cars. Localization systems that do not rely on GNSS typically require a map of the environment to compare the local sensor readings to the map. In most cases, building such a model requires an explicit mapping phase for recording sensor data in the environment. In this paper, we investigate the problem of localizing a mobile vehicle equipped with a 3D LiDAR scanner, driving on urban roads without mapping the environment beforehand. We propose an approach that builds upon publicly available map information from OpenStreetMap and turns them into a compact map representation that can be used for Monte Carlo localization. This map requires to store only a tiny 4-bit descriptor per location and is still able to globally localize and track a vehicle. We implemented our approach and thoroughly tested it on real-world data using the KITTI datasets. The experiments presented in this paper suggest that we can estimate the vehicle pose effectively only using OpenStreetMap data. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ECMR.2019.8870918 | 2019 European Conference on Mobile Robots (ECMR) |
Keywords | Field | DocType |
mobile vehicle,autonomous cars,localization systems,GNSS,local sensor readings,explicit mapping phase,sensor data,3D LiDAR scanner,urban roads,publicly available map information,compact map representation,Monte Carlo localization,4-bit descriptor,real-world data,OpenStreetMap data,global localization,4-bit semantic descriptors | Computer vision,4-bit,Computer science,Robot kinematics,Lidar,Scanner,Artificial intelligence,GNSS applications,Robot,Monte Carlo localization,Semantics | Conference |
ISBN | Citations | PageRank |
978-1-7281-3606-6 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Yan | 1 | 1 | 0.36 |
| Olga Vysotska | 2 | 34 | 3.47 |
| Cyrill Stachniss | 3 | 3975 | 224.13 |