@inproceedings{EichenbaumNikolovskiMuelhensetal.2023,
  author    = {Eichenbaum, Julian and Nikolovski, Gjorgji and M{\"u}lhens, Leon and Reke, Michael and Ferrein, Alexander and Scholl, Ingrid},
  title     = {Towards a lifelong mapping approach using Lanelet 2 for autonomous open-pit mine operations},
  series = {2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)},
  booktitle = {2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)},
  publisher = {IEEE},
  isbn      = {979-8-3503-2069-5 (Online)},
  doi       = {10.1109/CASE56687.2023.10260526},
  pages     = {8 Seiten},
  year      = {2023},
  abstract  = {Autonomous agents require rich environment models for fulfilling their missions. High-definition maps are a well-established map format which allows for representing semantic information besides the usual geometric information of the environment. These are, for instance, road shapes, road markings, traffic signs or barriers. The geometric resolution of HD maps can be as precise as of centimetre level. In this paper, we report on our approach of using HD maps as a map representation for autonomous load-haul-dump vehicles in open-pit mining operations. As the mine undergoes constant change, we also need to constantly update the map. Therefore, we follow a lifelong mapping approach for updating the HD maps based on camera-based object detection and GPS data. We show our mapping algorithm based on the Lanelet 2 map format and show our integration with the navigation stack of the Robot Operating System. We present experimental results on our lifelong mapping approach from a real open-pit mine.},
  language  = {en}
}