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@PHDTHESIS{Lin:855014,
      author       = {Lin, Jiaying},
      othercontributors = {Abel, Dirk and Schön, Steffen},
      title        = {{P}erception and observation with networked multi-agent
                      systems for automated shipping and harbor applications},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2022-09930},
      pages        = {1 Online-Ressource : Illustrationen, Diagramme},
      year         = {2022},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2022},
      abstract     = {Environment perception is a fundamental element of
                      automated vessels, especially in hightraffic areas such as
                      harbors. The intelligent vessels should be aware of the
                      situation, i.e., perceive and observe the objects in the
                      environment, to avoid collisions while conducting highly
                      automated tasks, such as autonomous docking and cooperative
                      maneuvering. In the context of an intelligent harbor for
                      future automated shipping, the vessels must be wirelessly
                      connected for real-time information exchange and thus a
                      robust cooperative localization and perception. Meanwhile,
                      environment perception and observation based on sensor
                      fusion have drawn much attention for maritime applications.
                      However, few studies intelligently integrate various
                      perception sensors, considering their characteristics. As
                      for the perception and localization of networked multi-agent
                      systems, few approaches exist to tackle the real world’s
                      maritime scenarios. This thesis proposes a novel concept of
                      environment perception and observation of multiagent systems
                      for automated maritime applications. This approach uses
                      Light Detection and Ranging (LiDAR) as a primary sensor,
                      Automatic Identification Systems (AIS), and Radio Detection
                      and Ranging System (radar) as assisting information sources.
                      It consists of four functional modules: object detection,
                      Multi-Object Tracking (MOT), static environment mapping, and
                      networked localization. For detecting objects in the
                      surroundings, a Convolutional Neural Network (CNN) is
                      applied to recognize different patterns in LiDAR point
                      clouds, extract objects from them, and generate bounding
                      boxes for the detected objects. The detected objects are
                      tracked by estimating their motion profile for possible
                      collision avoidance in the MOT module, which integrates the
                      detections from different perception sensor measurements. As
                      for static mapping, several polygons are used to represent
                      the static environment. In networked localization, the
                      perception from a single vessel is integrated into a central
                      server, such that more networked vessels can share and
                      optimize their perception estimation. The proposed
                      algorithms were evaluated with test drives conducted in
                      Rostock harbor, Germany. The research vessels equipped with
                      navigation and perception sensors carried out different
                      driving scenarios, such as docking and maneuvering, in which
                      promising performance of the algorithms was demonstrated.
                      The proposed perception and observation of networked
                      multi-agent systems present a possibility of highly accurate
                      and robust surveillance in a connected, intelligent harbor.},
      cin          = {416610},
      ddc          = {670},
      cid          = {$I:(DE-82)416610_20140620$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2022-09930},
      url          = {https://publications.rwth-aachen.de/record/855014},
}