Detail publikace

Fusing the RGBD SLAM with Wheel Odometry

Originální název

Fusing the RGBD SLAM with Wheel Odometry

Anglický název

Fusing the RGBD SLAM with Wheel Odometry

Jazyk

en

Originální abstrakt

This paper deals with data fusion of existing visual SLAM algorithm and wheel odometry. The result of this connection is the possibility of suppressing measurement error of each position estimation method and creating more accurate 3D model of the examined environment. We have made a brief review of existing visual SLAM projects that are available in open-source domain and as the best option we have choose the Elastic Fusion project and we enriched the existing localization pipeline with another data source that is realized by our high-precision odometry vehicle frame. The modified technique has been validated on three different scenarios. The result is the more robust SLAM algorithm and better precision 3D model of sensed environment compared to original one.

Anglický abstrakt

This paper deals with data fusion of existing visual SLAM algorithm and wheel odometry. The result of this connection is the possibility of suppressing measurement error of each position estimation method and creating more accurate 3D model of the examined environment. We have made a brief review of existing visual SLAM projects that are available in open-source domain and as the best option we have choose the Elastic Fusion project and we enriched the existing localization pipeline with another data source that is realized by our high-precision odometry vehicle frame. The modified technique has been validated on three different scenarios. The result is the more robust SLAM algorithm and better precision 3D model of sensed environment compared to original one.

Dokumenty

BibTex


@inproceedings{BUT159593,
  author="Adam {Ligocki} and Aleš {Jelínek}",
  title="Fusing the RGBD SLAM with Wheel Odometry",
  annote="This paper deals with data fusion of existing visual SLAM algorithm and wheel odometry. The result of this connection is the possibility of suppressing measurement error of each position estimation method and creating more accurate 3D model of the examined environment. We have made a brief review of existing visual SLAM projects that are available in open-source domain and as the best option we have choose the Elastic Fusion project and we enriched the existing localization pipeline with another data source that is realized by our high-precision odometry vehicle frame. The modified technique has been validated on three different scenarios. The result is the more robust SLAM algorithm and better precision 3D model of sensed environment compared to original one.",
  booktitle="PDeS sborník",
  chapter="159593",
  doi="10.1016/j.ifacol.2019.12.724",
  howpublished="electronic, physical medium",
  year="2019",
  month="october",
  pages="7--12"
}