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基于扩频超声和飞行时间相机的无人机三维室内定位

3D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras.

作者信息

Paredes José A, Álvarez Fernando J, Aguilera Teodoro, Villadangos José M

机构信息

Sensory System Research Group, University of Extremadura, 06006 Badajoz, Spain.

Department of Electronics, University of Alcalá, Alcalá de Henares, 28805 Madrid, Spain.

出版信息

Sensors (Basel). 2017 Dec 30;18(1):89. doi: 10.3390/s18010089.

DOI:10.3390/s18010089
PMID:29301211
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5795612/
Abstract

This work proposes the use of a hybrid acoustic and optical indoor positioning system for the accurate 3D positioning of Unmanned Aerial Vehicles (UAVs). The acoustic module of this system is based on a Time-Code Division Multiple Access (T-CDMA) scheme, where the sequential emission of five spread spectrum ultrasonic codes is performed to compute the horizontal vehicle position following a 2D multilateration procedure. The optical module is based on a Time-Of-Flight (TOF) camera that provides an initial estimation for the vehicle height. A recursive algorithm programmed on an external computer is then proposed to refine the estimated position. Experimental results show that the proposed system can increase the accuracy of a solely acoustic system by 70-80% in terms of positioning mean square error.

摘要

这项工作提出使用一种混合声学和光学的室内定位系统,用于无人机(UAV)的精确三维定位。该系统的声学模块基于时分码分多址(T-CDMA)方案,其中通过执行五个扩频超声码的顺序发射,按照二维多边测量程序来计算飞行器的水平位置。光学模块基于飞行时间(TOF)相机,它为飞行器高度提供初始估计。然后提出一种在外部计算机上编程的递归算法,以优化估计位置。实验结果表明,就定位均方误差而言,所提出的系统可使单一声学系统的精度提高70%-80%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/205a957a50db/sensors-18-00089-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/148577c3d155/sensors-18-00089-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/8c46d30c50cf/sensors-18-00089-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/7dcbb5e690e3/sensors-18-00089-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/0813a9517c20/sensors-18-00089-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/77bd60357d65/sensors-18-00089-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/12d47e5affbe/sensors-18-00089-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/8ee18444f318/sensors-18-00089-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/6c36b8789123/sensors-18-00089-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/97af5a18c5c9/sensors-18-00089-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/205a957a50db/sensors-18-00089-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/148577c3d155/sensors-18-00089-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/8c46d30c50cf/sensors-18-00089-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/7dcbb5e690e3/sensors-18-00089-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/0813a9517c20/sensors-18-00089-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/77bd60357d65/sensors-18-00089-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/12d47e5affbe/sensors-18-00089-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/8ee18444f318/sensors-18-00089-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/6c36b8789123/sensors-18-00089-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/97af5a18c5c9/sensors-18-00089-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f1c/5795612/205a957a50db/sensors-18-00089-g010.jpg

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Sensors (Basel). 2017 Jun 2;17(6):1268. doi: 10.3390/s17061268.
3
A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments.
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4
System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs.应用于六旋翼无人机的系统辨识与带避碰功能的非线性模型预测控制
Sensors (Basel). 2022 Jun 22;22(13):4712. doi: 10.3390/s22134712.
5
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Sensors (Basel). 2022 Jun 19;22(12):4622. doi: 10.3390/s22124622.
6
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7
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8
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9
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10
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Sensors (Basel). 2017 Apr 8;17(4):802. doi: 10.3390/s17040802.
4
Sensors for 3D Imaging: Metric Evaluation and Calibration of a CCD/CMOS Time-of-Flight Camera.3D 成像传感器:CCD/CMOS 飞行时间相机的度量评估和校准。
Sensors (Basel). 2009;9(12):10080-96. doi: 10.3390/s91210080. Epub 2009 Dec 11.