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@PHDTHESIS{Quack:803846,
      author       = {Quack, Tobias Michael},
      othercontributors = {Abel, Dirk and Eckstein, Lutz},
      title        = {{A}utomatische {A}ktualisierung digitaler {K}arten für die
                      hochgenaue {L}okalisierung autonomer {F}ahrzeuge},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      reportid     = {RWTH-2020-09922},
      pages        = {1 Online-Ressource (xvi, 135 Seiten) : Illustrationen,
                      Diagramme},
      year         = {2020},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2020},
      abstract     = {A major prerequisite for autonomous driving is precise and
                      reliable vehicle self-localization. Currently available
                      solutions for this problem are mostly based on satellite
                      navigation systems which suffer from several inherent
                      deficiencies especially in challenging urban traffic
                      scenarios. In recent years, research has therefore been
                      conducted on matching sensor data from the vehicle’s
                      environment perception systems with high-definition digital
                      maps in order to achieve accurate self-localization. One
                      unsolved problem with this approach concerns the
                      obsolenscence of the map data in dynamic traffic
                      environments. Particularly in urban scenarios, environment
                      features detectable by the vehicle’s sensor systems are
                      regularly subject to change so that a digital map suitable
                      as a reference for localization must be updated
                      continuously. The focus of this dissertation lies in the
                      development of a system for automated updating of digital
                      maps for the highly accurate localization of autonomous
                      vehicles. The system requires a connected traffic
                      environment in which vehicles and infrastructure are able to
                      communicate. On the infrastructure side, a data processing
                      system automatically evaluates sensor data provided by the
                      connected vehicles in order to continuously update a digital
                      map of the respective traffic area. The map should include
                      all objects that remain stationary for at least several
                      minutes and which are thus useable for self-localization of
                      following vehicles. The key methods used here are
                      graph-based optimization and automatic alignment of lidar
                      point clouds as well as grid-based probabilistic mapping
                      approaches. The second emphasis of this work is the
                      development of the vehicle system that achieves accurate and
                      robust real-time localization based on the digital map. The
                      main sensor on the vehicle side is a scanning lidar with a
                      horizontal field of view of 360°. In addition, the system
                      fuses data from an inertial sensor and wheel speed sensors
                      in order to provide estimations for the vehicle position and
                      orientation at a rate of 50 Hz. The key method for the
                      vehicle localization is an adapted particle filter with a
                      dynamic vehicle model. After discussing the methods for
                      mapping and localization, this dissertation also includes an
                      experimental validation of the connected system in an urban
                      test scenario.},
      cin          = {416610},
      ddc          = {620},
      cid          = {$I:(DE-82)416610_20140620$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2020-09922},
      url          = {https://publications.rwth-aachen.de/record/803846},
}