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“Unscented Multi-Point Smoother for Fusion of Delayed Displacement Measurements: Application to Agricultural Robots”

Authors: Mathias Hauan Arbo, Trygve Utstumo, Edmund Brekke and Jan T. Gravdahl,
Affiliation: NTNU, Department of Engineering Cybernetics and Adigo AS
Reference: 2017, Vol 38, No 1, pp. 1-9.

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Keywords: Robot Navigation, Sensor fusion, Agricultural Robotics

Abstract: Visual Odometry (VO) is increasingly a useful tool for robotic navigation in a variety of applications, including weed removal for agricultural robotics. The methods of evaluating VO are often computationally expensive and can cause the VO measurements to be significantly delayed with respect to a compass, wheel odometry, and GPS measurements. In this paper we present a Bayesian formulation of fusing delayed displacement measurements. We implement solutions to this problem based on the unscented Kalman filter (UKF), leading to what we term an unscented multi-point smoother. The proposed methods are tested in simulations of an agricultural robot. The simulations show improvements in the localization RMS error when including the VO measurements with a variety of latencies.

PDF PDF (1221 Kb)        DOI: 10.4173/mic.2017.1.1



DOI forward links to this article:
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BibTeX:
@article{MIC-2017-1-1,
  title={{Unscented Multi-Point Smoother for Fusion of Delayed Displacement Measurements: Application to Agricultural Robots}},
  author={Arbo, Mathias Hauan and Utstumo, Trygve and Brekke, Edmund and Gravdahl, Jan T.},
  journal={Modeling, Identification and Control},
  volume={38},
  number={1},
  pages={1--9},
  year={2017},
  doi={10.4173/mic.2017.1.1},
  publisher={Norwegian Society of Automatic Control}
};

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