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Spline Error Weighting for Robust Visual-Inertial Fusion

Hannes Ovrén, Per-Erik Forssén
CVPR18, Salt Lake City, Utah, USA
IEEE Conference on Computer Vision and Pattern Recognition
June 2018


In this paper we derive and test a probability-based weighting that can balance residuals of different types in spline fitting. In contrast to previous formulations, the proposed spline error weighting scheme also incorporates a prediction of the approximation error of the spline fit. We demonstrate the effectiveness of the prediction in a synthetic experiment, and apply it to visual-inertial fusion on rolling shutter cameras. This results in a method that can estimate 3D structure with metric scale on generic first-person videos. We also propose a quality measure for spline fitting, that can be used to automatically select the knot spacing. Experiments verify that the obtained trajectory quality corresponds well with the requested quality. Finally, by linearly scaling the weights, we show that the proposed spline error weighting minimizes the estimation errors on real sequences, in terms of scale and end-point errors.

Full Paper

Open access paper on the CVF site [PDF] [suppl].
Source code on Github. See the Kontiki toolkit.
Video on YouTube.

Bibtex entry

  author = 	 {Hannes Ovr\'en and Per-Erik Forss\'en},
  title = 	 {Spline Error Weighting for Robust Visual-Inertial Fusion},
  booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition},
  year = 	 {2018},
  month = 	 {June},
  address = 	 {Salt Lake City, Utah, USA},
  publisher =    {Computer Vision Foundation},
  note = 	 {VR Project: Learnable Camera Motion Models, 2014-5928}

Per-Erik Forssén

Per-Erik Forssén


Computer Vision Laboratory
Department of Electrical Engineering
Building B
Room 2D:521
SE-581 83 Linköping, Sweden
+46(0)13 285654

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