  A key capability for autonomous underground mining vehicles is real-time
accurate localisation. While significant progress has been made, currently
deployed systems have several limitations ranging from dependence on costly
additional infrastructure to failure of both visual and range sensor-based
techniques in highly aliased or visually challenging environments. In our
previous work, we presented a lightweight coarse vision-based localisation
system that could map and then localise to within a few metres in an
underground mining environment. However, this level of precision is
insufficient for providing a cheaper, more reliable vision-based automation
alternative to current range sensor-based systems. Here we present a new
precision localisation system dubbed "LookUP", which learns a
neural-network-based pixel sampling strategy for estimating homographies based
on ceiling-facing cameras without requiring any manual labelling. This new
system runs in real time on limited computation resource and is demonstrated on
two different underground mine sites, achieving real time performance at ~5
frames per second and a much improved average localisation error of ~1.2 metre.
