LookUP: 天井カメラのみで実現する鉱山車両のリアルタイム高精度自己位置推定
LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles
坑内鉱山車両向けに、天井向きカメラの映像のみからニューラルネットで画素サンプリング戦略を学習しホモグラフィを推定する高精度自己位置推定システムを提案。手動ラベリング不要で約5fpsのリアルタイム動作と平均誤差約1.2mを2つの実鉱山で達成した。
著者: Fan Zeng, Adam Jacobson, David Smith, Nigel Boswell, Thierry Peynot, Michael Milford
分類: cs.RO
原文アブストラクト
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.
