Joint graph optimization towards crowd based mapping
| Autor: | Wenbing Zhang, Frank Schuster, Christoph G. Keller, Martin Haueis, Cristóbal Curio |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Ground truth Computer science Real-time computing Terrain 02 engineering and technology law.invention 020901 industrial engineering & automation law 0202 electrical engineering electronic engineering information engineering Parking lot 020201 artificial intelligence & image processing Joint (building) Graph optimization Radar |
| Zdroj: | ITSC |
| DOI: | 10.1109/itsc.2017.8317727 |
| Popis: | In any autonomous driving system, the map for localization plays a vital part that is often underestimated. The map describes the world around the vehicle outside of the sensor view and is a main input into the decision making process in highly complicated scenarios. Thus there are strict requirements towards the accuracy and timeliness of the map. We present a robust and reliable approach towards crowd based mapping using a GraphSLAM framework based on radar sensors. We show on a parking lot that even in dynamically changing environments, the localization results are very accurate and reliable even in unexplored terrain without any map data. This can be achieved by collaborative map updates from multiple vehicles. To show these claims experimentally, the Joint Graph Optimization is compared to the ground truth on an industrial parking space. Mapping performance is evaluated using a dense map from a total station as reference and localization results are compared with a deeply coupled DGPS/INS system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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