L
Lukas Polok
Researcher at Brno University of Technology
Publications - 28
Citations - 314
Lukas Polok is an academic researcher from Brno University of Technology. The author has contributed to research in topics: Sparse matrix & Structure from motion. The author has an hindex of 8, co-authored 28 publications receiving 273 citations. Previous affiliations of Lukas Polok include Multimedia University & Winthrop University.
Papers
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Journal ArticleDOI
SLAM++1-A highly efficient and temporally scalable incremental SLAM framework
TL;DR: A general framework for incremental maximum likelihood estimation called SLAM++ is introduced, which fully benefits from the incremental nature of the online applications, and provides efficient estimation of both the mean and the covariance of the estimate.
Proceedings ArticleDOI
Incremental Block Cholesky Factorization for Nonlinear Least Squares in Robotics
TL;DR: Online, incremental solutions, which take full advantage of the sparseblock structure of the problems in robotics, are introduced and the implementation outperforms the state of the art SLAM implementations on all the tested datasets.
Proceedings ArticleDOI
Fast covariance recovery in incremental nonlinear least square solvers
TL;DR: The main contribution of this paper is a novel algorithm for fast incremental covariance update, complemented by a highly efficient implementation of the covariance recovery, which yields to two orders of magnitude reduction in computation time, compared to the other state of the art solutions.
Proceedings ArticleDOI
Efficient implementation for block matrix operations for nonlinear least squares problems in robotic applications
TL;DR: This work exploits the block structure of such problems and offers very efficient solutions to manipulate block matrices within iterative nonlinear solvers and considerably speeds-up the execution of the implementation of the nonlinear optimization problem.
Proceedings ArticleDOI
Fast Incremental Bundle Adjustment with Covariance Recovery
TL;DR: This paper provides novel and efficient solutions to solving the associated NLS incrementally, and to compute not only the optimal solution, but also the associated uncertainty.