TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Schuster, Frank
A1  - Zhang, Wei
A1  - Keller, Christoph
A1  - Haueis, Martin
A1  - Curio, Cristóbal
T1  - Joint graph optimization towards crowd based mapping
T2  - IEEE ITSC 2017 : 20th International Conference on Intelligent Transportation Systems : Mielparque Yokohama in Yokohama, Kanagawa, Japan, October 16-19, 2017
N2  - 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.
KW  - Verkehrsleitsystem
Y1  - 2017
SN  - 978-1-5386-1526-3
SB  - 978-1-5386-1526-3
U6  - https://doi.org/10.1109/ITSC.2017.8317727
DO  - https://doi.org/10.1109/ITSC.2017.8317727
SP  - 179
EP  - 184
S1  - 6
PB  - IEEE
CY  - Piscataway, NJ
ER  -