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Joint graph optimization towards crowd based mapping

  • 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.

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Metadaten
Author of HS ReutlingenCurio, Cristóbal
DOI:https://doi.org/10.1109/ITSC.2017.8317727
ISBN:978-1-5386-1526-3
Erschienen in:IEEE ITSC 2017 : 20th International Conference on Intelligent Transportation Systems : Mielparque Yokohama in Yokohama, Kanagawa, Japan, October 16-19, 2017
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Year of Publication:2017
Tag:Verkehrsleitsystem
Page Number:6
First Page:179
Last Page:184
DDC classes:620 Ingenieurwissenschaften und zugeordnete Tätigkeitenn
Open Access?:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE