The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics. In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.
Creating multi-temporal maps of urban environments of improved localization of autonomous vehicles
2020-01-01
XXIV ISPRS Congress, Commission II : edition 2020 ; ISPRS Archives ; 43,B2
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Low-Cost Autonomous Vehicles for Urban Environments
SAE Technical Papers | 2008
|Low-Cost Autonomous Vehicles for Urban Environments
British Library Conference Proceedings | 2008
|Robust vehicle localization in urban environments using probabilistic maps
Tema Archiv | 2010
|