Abstract
The robust tracking of point features throughout an image sequence is one fundamental stage in many different computer vision algorithms (e.g. visual modelling, object tracking, etc.). In most cases, this tracking is realised by means of a feature detection step and then a subsequent re-identification of the same feature point, based on some variant of a template matching algorithm. Without any auxiliary knowledge about the movement of the camera, actual tracking techniques are only robust for relatively moderate frame-to-frame feature displacements. This paper presents a framework for a visual-inertial feature tracking scheme, where images and measurements of an inertial measurement unit (IMU) are fused in order to allow a wider range of camera movements. The inertial measurements are used to estimate the visual appearance of a feature’s local neighbourhood based on a affine photometric warping model.
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Notes
- 1.
MEMS—micro-electromechanical systems.
- 2.
The different sensor types are indicated by the subscript indices at the entities in the different equations.
- 3.
m des describes the magnitude of the earth’s magnetic field (e.g. 48 \(\mu T\) in Western Europe).
- 4.
For simplification: \(s\alpha=sin(\alpha)\) and \(c\beta=cos(\beta)\).
- 5.
For this a simple first-order Taylor expansion of the minimisation term is used.
- 6.
Here a successfully tracked feature is a feature which is not neglected based on the error threshold e limit .
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Aufderheide, D., Edwards, G., Krybus, W. (2015). Visual-Inertial 2D Feature Tracking based on an Affine Photometric Model. In: Tavares, J., Natal Jorge, R. (eds) Developments in Medical Image Processing and Computational Vision. Lecture Notes in Computational Vision and Biomechanics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-13407-9_18
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