LookUP：用于自主采矿车辆的实时的精确地下定位
LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles
摘要：
自主地下采矿车辆的关键能力是实时的精确定位。虽然已经取得了重大进展，目前部署的系统有几个限制，从依赖昂贵的额外基础设施，以失败的视觉和距离传感器为基础的技术在高度混淆或视觉挑战的环境。在我们之前的工作中，我们提出了一个轻量级的基于粗略视觉的定位系统，可以在地下采矿环境中映射并定位到几米以内。然而，这种精度水平不足以为当前基于距离传感器的系统提供更便宜、更可靠的基于视觉的自动化替代方案。在这里，我们提出了一个新的精确定位系统被称为“LookUP”，它学习基于神经网络的像素采样策略，用于基于面向天花板的摄像头估计单应性，而不需要任何手动标签。这个新系统在有限的计算资源上实时的运行，并在两个不同的地下矿场上进行了演示，实现了每秒约5帧的实时的性能，平均定位误差大大改善，为约1.2米。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
