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Indoor Localization Using Inertial Navigation Systems

机译：使用惯性导航系统的室内定位

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摘要

Localizing objects in an environment is very important as a lot of tasks depend upon the presence of objects at certain points. Outdoor localization can be easily done using global navigation satellite system (GNSS). This method does not ensure certain accuracy for an indoor environment. A host of indoor localization techniques are available to improve accuracy. Most of them involve installing certain equipment (transmitter and receiver nodes). This thesis investigates an approach based on an inertial measurement unit (IMU) that doesn't involve any equipment installation and can track objects in an indoor environment with precision. The IMU used includes 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer. The accelerometer and gyroscope measure multiple forces that act on the sensor making the measured data noisy. Different filters including 6th order lowpass Butterworth filter, denoised filter, and median filter are used to filter out the noise without effecting the shape of the original signal. The magnetometer measures the magnetic field in all directions providing the absolute magnetic north. All sensors are calibrated to eliminate any bias including acceleration due to gravity measurement by the accelerometer at rest, zero angular velocity by the gyroscope, and heading correctness for the magnetometer.;A complimentary filter is used to estimate the orientation of the object by fusing the data from the accelerometer, gyroscope, and magnetometer. Static acceleration components (gravity and static noises) are removed from its orientation. The Kalman filter is used to predict the position of the object using the dynamic acceleration and dead reckoning technique. GPS data are also incorporated to provide an initial position and reduce the chances of drift caused by dead reckoning.;The algorithm was tested in real-time, the raw data were logged for different motions and implemented in MATLAB to predict the position of the object. At the end, algorithm was implemented on data collected from three different IMU devices including standalone MPU9255 sensor, iPhone 7, and Jackal Robot. The proposed algorithm shows similar position accuracy compare to other indoor tracking techniques that require equipment installation.
机译：在环境中对对象进行本地化非常重要，因为许多任务取决于特定点上对象的存在。使用全球导航卫星系统（GNSS）可以轻松完成室外定位。此方法不能确保室内环境的某些准确性。许多室内定位技术可用来提高准确性。其中大多数涉及安装某些设备（发送方和接收方节点）。本文研究了一种基于惯性测量单元（IMU）的方法，该方法不涉及任何设备安装，并且可以精确地跟踪室内环境中的物体。使用的IMU包括3轴加速度计，3轴陀螺仪和3轴磁力计。加速度计和陀螺仪可测量作用在传感器上的多个力，从而使测量数据产生噪声。包括6阶低通巴特沃斯滤波器，降噪滤波器和中值滤波器在内的各种滤波器用于滤除噪声，而不会影响原始信号的形状。磁力计测量所有方向上的磁场，从而提供绝对的北磁。所有传感器均经过校准以消除任何偏差，包括加速度计在静止状态下因重力测量而产生的加速度，陀螺仪的零角速度以及磁力计的航向正确性;附加的滤波器用于通过融合物体来估计物体的方向来自加速度计，陀螺仪和磁力计的数据。从其方向移除了静态加速度分量（重力和静态噪声）。卡尔曼滤波器用于通过动态加速度和航位推算技术预测物体的位置。还集成了GPS数据以提供初始位置并减少由航位推测法引起的漂移的机会。；该算法进行了实时测试，记录了原始数据的不同运动，并在MATLAB中实现以预测对象的位置。最后，对从三种不同IMU设备（包括独立的MPU9255传感器，iPhone 7和Jackal Robot）收集的数据实施了算法。与需要安装设备的其他室内跟踪技术相比，该算法显示出相似的位置精度。

著录项

  • 作者

    Ahmad, Touqeer.;

  • 作者单位

    Stevens Institute of Technology.;

  • 授予单位 Stevens Institute of Technology.;
  • 学科 Electrical engineering.;Engineering.
  • 学位 Masters
  • 年度 2017
  • 页码 67 p.
  • 总页数 67
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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