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@ARTICLE{Renner:1028217,
      author       = {Renner, Alpha and Supic, Lazar and Danielescu, Andreea and
                      Indiveri, Giacomo and Frady, E. Paxon and Sommer, Friedrich
                      T. and Sandamirskaya, Yulia},
      title        = {{V}isual odometry with neuromorphic resonator networks},
      journal      = {Nature machine intelligence},
      volume       = {6},
      number       = {6},
      issn         = {2522-5839},
      address      = {London},
      publisher    = {Springer Nature Publishing},
      reportid     = {FZJ-2024-04411},
      pages        = {653–663},
      year         = {2024},
      abstract     = {Visual odometry (VO) is a method used to estimate
                      self-motion of a mobile robot using visual sensors. Unlike
                      odometry based on integrating differential measurements that
                      can accumulate errors, such as inertial sensors or wheel
                      encoders, VO is not compromised by drift. However,
                      image-based VO is computationally demanding, limiting its
                      application in use cases with low-latency, low-memory and
                      low-energy requirements. Neuromorphic hardware offers
                      low-power solutions to many vision and artificial
                      intelligence problems, but designing such solutions is
                      complicated and often has to be assembled from scratch. Here
                      we propose the use of vector symbolic architecture (VSA) as
                      an abstraction layer to design algorithms compatible with
                      neuromorphic hardware. Building from a VSA model for scene
                      analysis, described in our companion paper, we present a
                      modular neuromorphic algorithm that achieves
                      state-of-the-art performance on two-dimensional VO tasks.
                      Specifically, the proposed algorithm stores and updates a
                      working memory of the presented visual environment. Based on
                      this working memory, a resonator network estimates the
                      changing location and orientation of the camera. We
                      experimentally validate the neuromorphic VSA-based approach
                      to VO with two benchmarks: one based on an event-camera
                      dataset and the other in a dynamic scene with a robotic
                      task.},
      cin          = {PGI-15},
      ddc          = {004},
      cid          = {I:(DE-Juel1)PGI-15-20210701},
      pnm          = {5234 - Emerging NC Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001258009300004},
      doi          = {10.1038/s42256-024-00846-2},
      url          = {https://juser.fz-juelich.de/record/1028217},
}