Detail publikace

Methods for Simultaneous self Localization and Mapping for Depth Cameras

LIGOCKI, A.

Originální název

Methods for Simultaneous self Localization and Mapping for Depth Cameras

Anglický název

Methods for Simultaneous self Localization and Mapping for Depth Cameras

Jazyk

en

Originální abstrakt

This work deals with the extension of the existing implementation of RGBD Visual SLAM with additional data source from wheel odometry of robot’s chassis, on which RGBD sensor is lo- cated. Each of these two position estimation methods has a different character measurement uncer- tainty. By combining these methods together we could be able to suppress the disadvantages of both approaches, and in the result we would be able to create more accurate model of the robot’s environ- ment, which was unknown at the beginning of the measurement. Also accuracy of position estimation in created model can be improved.

Anglický abstrakt

This work deals with the extension of the existing implementation of RGBD Visual SLAM with additional data source from wheel odometry of robot’s chassis, on which RGBD sensor is lo- cated. Each of these two position estimation methods has a different character measurement uncer- tainty. By combining these methods together we could be able to suppress the disadvantages of both approaches, and in the result we would be able to create more accurate model of the robot’s environ- ment, which was unknown at the beginning of the measurement. Also accuracy of position estimation in created model can be improved.

Dokumenty

BibTex


@inproceedings{BUT141431,
  author="Adam {Ligocki}",
  title="Methods for Simultaneous self Localization and Mapping for Depth Cameras",
  annote="This work deals with the extension of the existing implementation of RGBD Visual SLAM
with additional data source from wheel odometry of robot’s chassis, on which RGBD sensor is lo-
cated. Each of these two position estimation methods has a different character measurement uncer-
tainty. By combining these methods together we could be able to suppress the disadvantages of both
approaches, and in the result we would be able to create more accurate model of the robot’s environ-
ment, which was unknown at the beginning of the measurement. Also accuracy of position estimation
in created model can be improved.",
  booktitle="Sborník EEICT 2017",
  chapter="141431",
  howpublished="online",
  year="2017",
  month="april",
  pages="193--195",
  type="conference paper"
}