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A Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning

机译：基于异构传感系统的无人机室内定位方法

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摘要

The indoor environment has brought new challenges for micro Unmanned Aerial Vehicles (UAVs) in terms of their being able to execute tasks with high positioning accuracy. Conventional positioning methods based on GPS are unreliable, although certain circumstances of limited space make it possible to apply new technologies. In this paper, we propose a novel indoor self-positioning system of UAV based on a heterogeneous sensing system, which integrates data from a structured light scanner, ultra-wideband (UWB), and an inertial navigation system (INS). We made the structured light scanner, which is composed of a low-cost structured light and camera, ourselves to improve the positioning accuracy at a specified area. We applied adaptive Kalman filtering to fuse the data from the INS and UWB while the vehicle was moving, as well as Gauss filtering to fuse the data from the UWB and the structured light scanner in a hovering state. The results of our simulations and experiments demonstrate that the proposed strategy significantly improves positioning accuracy in motion and also in the hovering state, as compared to using a single sensor.
机译：室内环境给微型无人机（UAV）带来了新挑战，因为微型无人机能够执行高定位精度的任务。传统的基于GPS的定位方法是不可靠的，尽管在空间有限的某些情况下可以应用新技术。在本文中，我们提出了一种基于异质感测系统的新型无人机室内自定位系统，该系统集成了结构化光扫描仪，超宽带（UWB）和惯性导航系统（INS）的数据。我们自己制作了由低成本结构光和照相机组成的结构光扫描仪，目的是提高指定区域的定位精度。我们应用自适应卡尔曼滤波在车辆行驶时融合来自INS和UWB的数据，以及高斯滤波以融合来自UWB和结构化光扫描器的悬停状态。我们的仿真和实验结果表明，与使用单个传感器相比，所提出的策略显着提高了运动中以及悬停状态下的定位精度。

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