| 基于UWB/IMU融合的室内定位与导航技术研究 | |
Gao Ouyang
| |
| 2017-05 | |
| Subtype | 工程硕士 |
| Abstract | 现如今，室内定位技术在诸如无人机、目标跟踪、虚拟现实/增强实现、扫地机器人、服务机器人、无人驾驶等领域占据越来越重要的地位。准确的位置服务，可以为运动目标提供位姿信息及环境感知能力，引导其完成自动的路径规划。因此，对室内高精度定位技术的研究成为一个重要课题。 室内定位技术存在的技术缺陷在于，单一的定位系统无法达到实际的应用要求，或精度不够、或应用场景受限、或稳定性不足、或因成本昂贵无法普及等。为解决这一技术难题，本论文开展基于UWB(Ultra Wideband，超宽带)测距和惯性导航系统组合定位关键技术研究，并给出定位系统工程实施的一些想法和建议。为了实现多传感器融合算法，本文首先分析了单一定位系统的特征与固有缺陷，然后针对不同应用场景设计状态估计模型，包括多基站(Anchor)下无线定位与惯性导航的卡尔曼滤波算法、单基站与行人航迹推算系统的图优化模型等。下面按章节层次描述： 第一、论证场景内UWB基站部署个数、空间布局对于位置误差的影响，并就实际部署系统给出指导性方法和建议；对于低成本微机电系统的惯性测量单元，提出简单有效的参数标定方法，如静态零偏、随机噪声分析等； 第二、研究基于UWB测距和惯性导航组合定位的卡尔曼滤波模型，并对比可扩展卡尔曼滤波、无迹卡尔曼滤波性能，定位系统实现了正确判别无线信号固有的非视距(non-line-of-sight，NLOS)干扰和自适应滤波； 第三、 研究单基站与脚安装(Foot-Mounted)的行人航迹推算系统组合定位关键算法，该算法在最优化的因子图模型下，使用零速率更新和步态分割将惯性导航系统转化为较高精度的里程测量信息，加之UWB测距的位置约束，基本实现二维的位置输出。 |
| Other Abstract | Nowadays indoor positioning technology have become more and more popular in areas such as sweeping robots, service robots, virtual/augmented reality, pedestrian tracking and unmanned aerial vehicles etc. High-accurate location can provide moving target for the pose used to explore unknown environments and realize the automatic pilot or planning. Therefore, studying high-precision location indoor is a very important issue. The challenge of indoor localization system is that single-method positioning system are far away from the practical demands, such as not enough positional accuracy, limited applicable scenes, lack of stability, or high cost and so on. Then the paper hope to improve these shortcomings and carries out some researches on key techniques of localization system fusing ultra-wideband(UWB) ranging with inertial navigation system. To realize multi-sensor fusion, we firstly analyzes the characteristics and inherent defects of single-method positioning system, and then designs state estimation model for different scenarios including nonlinear Kalman filter algorithm for multi-anchor/inertial-measurement-unit localization system and optimization estimation model for single-anchor/ inertial-measurement-unit localization system. The following description in detail are below: Firstly, the paper proof how spatial-layout of anchors in the scenarios transfer ranging error to location error, and give some recommendations about actual deployment; The paper also put forward an easy and effective parameter calibration method, such as static zero bias calibration, noise component analysis, for low-cost MEMS(Micro-Electro-Mechanical System) inertial sensor; Secondly, study non-linear Kalman filter based on UWB ranging and inertial navigation system, compare the performance of Extended Kalman filter(EKF) to unscented Kalman filter(UKF), and realize accurate discrimination of non-line-of-sight(NLOS) that is specific for wireless signal and adaptive filtering. Thirdly, study the key algorithm of single-anchor and Foot-Mounted pedestrian dead reckoning system. The algorithm uses zero-velocity update and gait partition to transform inertial navigation system into odometer information, and use the graph optimization model to correct it, and basically realize 2D position service. |
| Keyword | Uwb定位、惯性导航、卡尔曼滤波器、图优化 |
| Subject Area | 第一研究方向 |
| Language | 中文 |
| Document Type | 学位论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/14725 |
| Collection | 毕业生_硕士学位论文 |
| Affiliation | 中科院自动化所研究所 |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Gao Ouyang. 基于UWB/IMU融合的室内定位与导航技术研究[D]. 北京. 中国科学院研究生院,2017. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 2014E8014661081欧阳高.p（4224KB） | 学位论文 | 限制开放 | CC BY-NC-SA | |||
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