Robustly and accurately localizing vehicles in underground mines is particularly challenging due to the unavailability of GPS, variable and often poor lighting conditions, visual aliasing in long tunnels, and airborne dust and water. In this paper, we present a novel, infrastructure-less, multisensor localization method for robust autonomous operation within underground mines. The proposed method integrates with existing mine site commissioning and operation procedures and includes both an offline map-building process and an online localization algorithm. The approach combines the strengths of visual-based place recognition, LIDAR-based localization, and odometry in a particle filter fusion process. We provide an extensive experimental validation using new large data sets acquired in two operational Australian underground hard-rock mines (including a 600m-deep multilevel mine with approximately 33 km of mapping data and 7 km of vehicle localization) by actual mining vehicles during production operations. We demonstrate a significant increase in localization accuracy over prior state-of-the-art SLAM research systems and real-time operation, with processing times in the order of 10 Hz. We present results showing a mean error of 0.68 m from the Queensland Mine data set and 1.32 m from the New South Wales Mine data set and at least 86% reduction in error compared with prior state of the art. We also analyze the impact of the particle filter parameters with respect to localization accuracy. Together this study represents a new approach to positioning systems for currently deployed autonomous vehicles within underground mine environments.

中文翻译：

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下面是什么定位：用于自动地下采矿车的度量多传感器定位和测绘系统

由于 GPS 不可用、多变且通常较差的照明条件、长隧道中的视觉混叠以及空气中的灰尘和水，在地下矿井中稳健准确地定位车辆尤其具有挑战性。在本文中，我们提出了一种新颖的、无基础设施的、多传感器定位方法，用于在地下矿井中进行稳健的自主操作。所提出的方法与现有的矿场调试和操作程序相结合，包括离线地图构建过程和在线定位算法。该方法在粒子滤波器融合过程中结合了基于视觉的位置识别、基于 LIDAR 的定位和里程计的优势。我们使用实际采矿车辆在生产运营期间从两个正在运营的澳大利亚地下硬岩矿山（包括一个 600m 深的多层矿山，具有约 33 公里的测绘数据和 7 公里的车辆定位）中获取的新大型数据集提供了广泛的实验验证. 我们证明了与先前最先进的 SLAM 研究系统和实时操作相比，定位精度显着提高，处理时间约为 10 Hz。我们展示的结果显示昆士兰矿数据集的平均误差为 0.68 m，新南威尔士矿数据集的平均误差为 1.32 m，与现有技术相比，误差至少减少 86%。我们还分析了粒子滤波器参数对定位精度的影响。