@InProceedings{Braining-etAl_ITSC2023_Extraction-HD-Maps,
  author       = {Braining, Andreas and Nikolovski, Gjorgji and Reke, Michael and Ferrein, Alexander},
  title        = {Extraction of Semantically Rich High-Definition Maps from Spatial Representations of an Open Pit Mine}, 
  booktitle    = {26th IEEE International Conference on Intelligent Transportation Systems (ITSC)}, 
  pages        = {4032--4039},
  year         = {2023},
  month        = {Sep.},
  day          = {24-28},
  location     = {Bilbao, Spain},
  doi          = {10.1109/ITSC57777.2023.10422269},
  url_ieeexpl  = {https://ieeexplore.ieee.org/abstract/document/10422269},
  ISSN         = {2153-0017},
  keywords     = {Point cloud compression;Measurement;Navigation;Urban areas;Spatial databases;Optimization;Testing},
  abstract     = {Hauling of material by automated vehicles can be one
                  of the sustainable solutions for the increasing
                  economic and environmental challenges in the mining
                  industry. For this, vehicles of various sizes must
                  drive through an ever-changing environment, due to
                  the destructive nature of resource
                  extraction. Therefore, we show our solution to
                  create automatically semantically rich
                  high-definition maps, which can be used for
                  efficient and safe navigation within the mine. In
                  contrast to navigation on pure spatial
                  geometry-based data like point clouds,
                  high-definition maps have the advantage, that
                  semantic information is recognised by the navigation
                  system. But manually created high-definition maps
                  need frequent updates due to the terrain changes,
                  which makes them inflexible for most
                  applications. In this paper, we will show, how
                  Lanelet2- maps can be generated automatically from
                  point clouds with a multistep algorithm and how
                  these maps are adjusted to the long-term changes of
                  the environment. For evaluation, we present our
                  findings in some real-world examples and
                  synthetically generated point clouds of the main
                  edge-case situations.},
}

{"_id":"uT2KWBPFiJQmLF9k9","bibbaseid":"braining-nikolovski-reke-ferrein-extractionofsemanticallyrichhighdefinitionmapsfromspatialrepresentationsofanopenpitmine-2023","author_short":["Braining, A.","Nikolovski, G.","Reke, M.","Ferrein, A."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"propositions":[],"lastnames":["Braining"],"firstnames":["Andreas"],"suffixes":[]},{"propositions":[],"lastnames":["Nikolovski"],"firstnames":["Gjorgji"],"suffixes":[]},{"propositions":[],"lastnames":["Reke"],"firstnames":["Michael"],"suffixes":[]},{"propositions":[],"lastnames":["Ferrein"],"firstnames":["Alexander"],"suffixes":[]}],"title":"Extraction of Semantically Rich High-Definition Maps from Spatial Representations of an Open Pit Mine","booktitle":"26th IEEE International Conference on Intelligent Transportation Systems (ITSC)","pages":"4032–4039","year":"2023","month":"Sep.","day":"24-28","location":"Bilbao, Spain","doi":"10.1109/ITSC57777.2023.10422269","url_ieeexpl":"https://ieeexplore.ieee.org/abstract/document/10422269","issn":"2153-0017","keywords":"Point cloud compression;Measurement;Navigation;Urban areas;Spatial databases;Optimization;Testing","abstract":"Hauling of material by automated vehicles can be one of the sustainable solutions for the increasing economic and environmental challenges in the mining industry. For this, vehicles of various sizes must drive through an ever-changing environment, due to the destructive nature of resource extraction. Therefore, we show our solution to create automatically semantically rich high-definition maps, which can be used for efficient and safe navigation within the mine. In contrast to navigation on pure spatial geometry-based data like point clouds, high-definition maps have the advantage, that semantic information is recognised by the navigation system. But manually created high-definition maps need frequent updates due to the terrain changes, which makes them inflexible for most applications. In this paper, we will show, how Lanelet2- maps can be generated automatically from point clouds with a multistep algorithm and how these maps are adjusted to the long-term changes of the environment. For evaluation, we present our findings in some real-world examples and synthetically generated point clouds of the main edge-case situations.","bibtex":"@InProceedings{Braining-etAl_ITSC2023_Extraction-HD-Maps,\n author = {Braining, Andreas and Nikolovski, Gjorgji and Reke, Michael and Ferrein, Alexander},\n title = {Extraction of Semantically Rich High-Definition Maps from Spatial Representations of an Open Pit Mine}, \n booktitle = {26th IEEE International Conference on Intelligent Transportation Systems (ITSC)}, \n pages = {4032--4039},\n year = {2023},\n month = {Sep.},\n day = {24-28},\n location = {Bilbao, Spain},\n doi = {10.1109/ITSC57777.2023.10422269},\n url_ieeexpl = {https://ieeexplore.ieee.org/abstract/document/10422269},\n ISSN = {2153-0017},\n keywords = {Point cloud compression;Measurement;Navigation;Urban areas;Spatial databases;Optimization;Testing},\n abstract = {Hauling of material by automated vehicles can be one\n of the sustainable solutions for the increasing\n economic and environmental challenges in the mining\n industry. For this, vehicles of various sizes must\n drive through an ever-changing environment, due to\n the destructive nature of resource\n extraction. Therefore, we show our solution to\n create automatically semantically rich\n high-definition maps, which can be used for\n efficient and safe navigation within the mine. In\n contrast to navigation on pure spatial\n geometry-based data like point clouds,\n high-definition maps have the advantage, that\n semantic information is recognised by the navigation\n system. But manually created high-definition maps\n need frequent updates due to the terrain changes,\n which makes them inflexible for most\n applications. In this paper, we will show, how\n Lanelet2- maps can be generated automatically from\n point clouds with a multistep algorithm and how\n these maps are adjusted to the long-term changes of\n the environment. For evaluation, we present our\n findings in some real-world examples and\n synthetically generated point clouds of the main\n edge-case situations.},\n}\n","author_short":["Braining, A.","Nikolovski, G.","Reke, M.","Ferrein, A."],"key":"Braining-etAl_ITSC2023_Extraction-HD-Maps","id":"Braining-etAl_ITSC2023_Extraction-HD-Maps","bibbaseid":"braining-nikolovski-reke-ferrein-extractionofsemanticallyrichhighdefinitionmapsfromspatialrepresentationsofanopenpitmine-2023","role":"author","urls":{" ieeexpl":"https://ieeexplore.ieee.org/abstract/document/10422269"},"keyword":["Point cloud compression;Measurement;Navigation;Urban areas;Spatial databases;Optimization;Testing"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"http://maskor.fh-aachen.de/biblio/MASKOR.bib","dataSources":["i8ftsMK5wMbiNqmtB","pBDNm3knLemYTNMHw"],"keywords":["point cloud compression;measurement;navigation;urban areas;spatial databases;optimization;testing"],"search_terms":["extraction","semantically","rich","high","definition","maps","spatial","representations","open","pit","mine","braining","nikolovski","reke","ferrein"],"title":"Extraction of Semantically Rich High-Definition Maps from Spatial Representations of an Open Pit Mine","year":2023}