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arXiv · 1708.08844

Semantic Texture for Robust Dense Tracking

Jan Czarnowski·Stefan Leutenegger·Andrew Davison
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Abstract

We argue that robust dense SLAM systems can make valuable use of the layers of features coming from a standard CNN as a pyramid of `semantic texture' which is suitable for dense alignment while being much more robust to nuisance factors such as lighting than raw RGB values. We use a straightforward Lucas-Kanade formulation of image alignment, with a schedule of iterations over the coarse-to-fine levels of a pyramid, and simply replace the usual image pyramid by the hierarchy of convolutional feature maps from a pre-trained CNN. The resulting dense alignment performance is much more robust to lighting and other variations, as we show by camera rotation tracking experiments on time-lapse sequences captured over many hours. Looking towards the future of scene representation for real-time visual SLAM, we further demonstrate that a selection using simple criteria of a small number of the total set of features output by a CNN gives just as accurate but much more efficient tracking performance.

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Jan Czarnowski, Stefan Leutenegger, Andrew Davison. 2017-08-29. Semantic Texture for Robust Dense Tracking. https://arxiv.org/abs/1708.08844

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