论文标题
基于学习的偏见校正了资源受限的移动机器人的超宽带定位
Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots
论文作者
论文摘要
准确的室内定位是许多机器人应用程序（从仓库管理到监视任务）的重要启用技术。超宽带（UWB）范围是一种有前途的解决方案，与替代性最先进的方法（例如同时定位和映射）相比，低成本，轻巧且计算便宜，使其特别适合资源受限的空中机器人。但是，许多商业上可用的超宽带无线电可提供不准确的有偏见范围测量值。在本文中，我们提出了一个偏差校正框架与两向范围和到达超宽带定位的时间差异兼容。我们的方法包括两个步骤：（i）统计异常值排斥和（ii）基于学习的偏见校正。这种方法是可扩展的，可以节俭，可以在板载纳米Quadcopter的微控制器上部署。先前的研究主要集中在双向偏斜校正上，尚未在闭环中实施，也没有使用资源受限的机器人。实验结果表明，使用我们的方法，定位误差降低了约18.5％和48％（分别为TWR和TDOA），而四轮驱动器只能通过仅来自UWB的位置信息来准确跟踪轨迹。
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) ranging is a promising solution which is low-cost, lightweight, and computationally inexpensive compared to alternative state-of-the-art approaches such as simultaneous localization and mapping, making it especially suited for resource-constrained aerial robots. Many commercially-available ultra-wideband radios, however, provide inaccurate, biased range measurements. In this article, we propose a bias correction framework compatible with both two-way ranging and time difference of arrival ultra-wideband localization. Our method comprises of two steps: (i) statistical outlier rejection and (ii) a learning-based bias correction. This approach is scalable and frugal enough to be deployed on-board a nano-quadcopter's microcontroller. Previous research mostly focused on two-way ranging bias correction and has not been implemented in closed-loop nor using resource-constrained robots. Experimental results show that, using our approach, the localization error is reduced by ~18.5% and 48% (for TWR and TDoA, respectively), and a quadcopter can accurately track trajectories with position information from UWB only.
