Detail publikace

Efficient Implementation for Block Matrix Operations Nonlinear Least Squares Problems for Robotic Applications

Originální název

Efficient Implementation for Block Matrix Operations Nonlinear Least Squares Problems for Robotic Applications

Anglický název

Efficient Implementation for Block Matrix Operations Nonlinear Least Squares Problems for Robotic Applications

Jazyk

en

Originální abstrakt

A large number of robotic, computer vision and computer graphics applications rely on efficiently solving the associated sparse linear system. Simultaneous localization and mapping (SLAM), structure from motion (SFM), non-rigid shape recovery, elastodynamic simulations, are only few examples in this direction. In general, those problems are non-linear and the solution can be approximated by incrementally solving a series of linearized problems. In some applications, the size of the systems might considerable affect the performance, especially when the sparsity is low. This paper exploits the block structure of such problems and offers efficient solutions to manipulate block matrices. In particular, we focus on testing the method on SLAM applications, but the applicability of the technique remains general.

Anglický abstrakt

A large number of robotic, computer vision and computer graphics applications rely on efficiently solving the associated sparse linear system. Simultaneous localization and mapping (SLAM), structure from motion (SFM), non-rigid shape recovery, elastodynamic simulations, are only few examples in this direction. In general, those problems are non-linear and the solution can be approximated by incrementally solving a series of linearized problems. In some applications, the size of the systems might considerable affect the performance, especially when the sparsity is low. This paper exploits the block structure of such problems and offers efficient solutions to manipulate block matrices. In particular, we focus on testing the method on SLAM applications, but the applicability of the technique remains general.

BibTex


@inproceedings{BUT103427,
  author="Lukáš {Polok} and Viorela Simona {Ila} and Marek {Šolony} and Pavel {Zemčík} and Pavel {Smrž}",
  title="Efficient Implementation for Block Matrix Operations Nonlinear Least Squares Problems for Robotic Applications",
  annote="A large number of robotic, computer vision and computer graphics applications
rely on efficiently solving the associated sparse linear system. Simultaneous
localization and mapping (SLAM), structure from motion (SFM), non-rigid shape
recovery, elastodynamic simulations, are only few examples in this direction. In
general, those problems are non-linear and the solution can be approximated by
incrementally solving a series of linearized problems. In some applications, the
size of the systems might considerable affect the performance, especially when
the sparsity is low. This paper exploits the block structure of such problems and
offers efficient solutions to manipulate block matrices. In particular, we focus
on testing the method on SLAM applications, but the applicability of the
technique remains general.",
  address="IEEE Computer Society",
  booktitle="Proceedings of 2013 IEEE International Conference on Robotics and Automation",
  chapter="103427",
  doi="10.1109/ICRA.2013.6630883",
  edition="NEUVEDEN",
  howpublished="electronic, physical medium",
  institution="IEEE Computer Society",
  year="2013",
  month="may",
  pages="123--131",
  publisher="IEEE Computer Society",
  type="conference paper"
}