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Creators/Authors contains: "Rubenstein, Michael"

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  1. ; ; (, IEEE transactions on robotics)
    Accepted Manuscript Full Text Available
  2. ; ; ; (, Robotics: Science and Systems Foundation)
    Full Text Available 
  3. ; ; ; ; ; (, IEEE Robotics and Automation Letters)
    Full Text Available 
  4. ; (, IEEE Robotics and Automation Letters)
    Full Text Available 
  5. ; ; ; ; (, IEEE Robotics and Automation Letters)
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  6. ; (, IEEE Robotics and Automation Letters)
    null (Ed.)
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  7. ; ; ; (, IEEE Transactions on Robotics)
    null (Ed.)
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  8. ; ; ; (, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
    null (Ed.)
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  9. ; ; ; (, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
    In this paper, we present CPL-Sync, a certifiably correct algorithm to solve planar pose graph optimization (PGO) using the complex number representation. We formulate planar PGO as the maximum likelihood estimation (MLE) on the product of unit complex numbers, and relax this nonconvex quadratic complex optimization problem to complex semidefinite programming (SDP). Furthermore, we simplify the corresponding semidefinite programming to Riemannian staircase optimization (RSO) on complex oblique manifolds that can be solved with the Riemannian trust region (RTR) method. In addition, we prove that the SDP relaxation and RSO simplification are tight as long as the noise magnitude is below a certain threshold. The efficacy of this work is validated through comparisons with existing methods as well as applications on planar PGO in simultaneous localization and mapping (SLAM), which indicates that the proposed algorithm is more efficient and capable of solving planar PGO certifiably. The C++ code for CPL-Sync is available at https://github. com/fantaosha/CPL- Sync. 
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