Abstract
Abstract. 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.
Highlights
Nowadays there is a strong trend towards automated or even autonomous driving
We were able to confirm all our hypotheses and have shown that our maps are suitable for precise localization and include seasonal effects
We want to combine the benefits of the seasonal maps with those of the comprehensive map created from all measurement epochs
Summary
Nowadays there is a strong trend towards automated or even autonomous driving. One important element for the development of autonomous vehicles are very precise and up-to-date models or maps of the environment. Other approaches model defined states of dynamic objects, for example open and closed doors (Stachniss, 2009) This reduces the complexity of the environment model, and is limited to a fixed set of states and can not adapt to unexpected changes, which makes such models unsuitable for uncontrolled outdoor environments. The map shall not interpolate between measurements Their approach does not explicitly model dynamic parts of the environment, but covers those by multiple temporal maps with different timescales. They test their map learning system in an indoor environment performing three runs per day over a period of five weeks
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