CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES

Schachtschneider, J.; Brenner, C.

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.

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Zitierform:

Schachtschneider, J. / Brenner, C.: CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES. 2020. Copernicus Publications.

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Rechteinhaber: J. Schachtschneider

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