  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.
