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

LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles