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首页> 外文学位 >Vehicle localization using low-accuracy GPS, IMU and map-aided vision.
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Vehicle localization using low-accuracy GPS, IMU and map-aided vision.

机译：使用低精度GPS，IMU和地图辅助视觉进行车辆定位。

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摘要

This dissertation describes the development of vehicle state estimation methods using low-cost sensors, their implementation, and comparison with highly accurate vehicle state estimators available today. This research was motivated by the problem of navigating a vehicle on a highway, where it is desirable to closely measure the vehicle's state (absolute position and orientation, rotation rates etc.) to achieve electronic stability control, collision avoidance, driver alert systems for lane departure and ultimately autonomous navigation. The focus in this thesis is to develop low-cost methods for vehicle localization. Low-cost Commercial Off-the-Shelf (COTS) sensor systems have been used to this effect.;A framework is developed to combine measurements from Global Positioning System (GPS) and Inertial Measurement Unit (IMU). Performance of a low-cost GPS receiver operating in autonomous mode integrated with a MEMS based low-cost IMU is investigated. The error sources in GPS and INS systems are characterized to choose suitable stochastic models for the error sources and to identify parameters for these models. Vehicle velocity vector is used to improve the yaw angle estimate under low yaw angle observability conditions.;To obtain an independent direct measurement of the vehicle orientation, a novel method based on terrain-aided vision is developed. This method is based on matching images captured from an on-vehicle camera to a rendered representation of the surrounding terrain obtained from an on-board map database. United States Geographical Survey Digital Elevation Maps (DEMs) were used to create a 3D topology map of the geography surrounding the vehicle. The horizon lines seen in the captured video from the vehicle are compared to the horizon lines obtained from the rendered geography, allowing absolute comparisons between rendered and actual scene in roll, pitch and yaw.;Work on terrain-aided vision based orientation estimation has been extended to use near field features like road signs and road markers. Near field features allow the measurement of vehicle position in addition to vehicle orientation. A map-aided vision algorithm is presented which registers features in the rendered images with features in real images using gradient-based minimization of sum of squared intensities. To improve the convergence properties as well as convergence time of the vision algorithm, an IMU is used to predict the location and possible variability of features in the rendered representation defining a Region-Of-Interest (ROI).;A Kalman filter framework is used to fuse the measurements from an IMU and each of the position and orientation estimation methods mentioned above. Numerical simulations are done in each case to verify the correctness of the formulation. Finally, experiments are performed at the Pennsylvania Transportation Institute (PTI) test track facility to test the performance of each method against a highly accurate GPS/IMU system.
机译：本文介绍了使用低成本传感器的车辆状态估计方法的发展，其实现方法以及与当今可用的高精度车辆状态估计器的比较。这项研究是出于在高速公路上驾驶车辆的问题而进行的，在该高速公路上，需要紧密测量车辆的状态（绝对位置和方向，转速等）以实现电子稳定性控制，避免碰撞，车道驾驶员预警系统出发并最终实现自主导航。本文的重点是开发低成本的车辆定位方法。为此，已经使用了低成本的商用现货（COTS）传感器系统。开发了一个框架，以结合来自全球定位系统（GPS）和惯性测量单元（IMU）的测量结果。研究了在自主模式下与基于MEMS的低成本IMU集成的低成本GPS接收机的性能。 GPS和INS系统中的误差源的特征是为误差源选择合适的随机模型，并为这些模型识别参数。在低偏航角可观测性条件下，使用车辆速度矢量来改善偏航角估计。为了获得车辆方向的独立直接测量值，开发了一种基于地形辅助视觉的新方法。该方法基于将从车载摄像机捕获的图像与从车载地图数据库获得的周围地形的渲染表示进行匹配。美国地理调查局数字高程图（DEM）用于创建车辆周围地理环境的3D拓扑图。将从车辆捕获的视频中看到的地平线与从渲染的地理区域获得的地平线进行比较，从而可以在滚动，俯仰和偏航方面对渲染的场景和实际场景进行绝对比较。；已经进行了基于地形辅助视觉的方向估计工作扩展为使用路标和路标等近场功能。除了车辆方向外，近场功能还可以测量车辆位置。提出了一种地图辅助视觉算法，该算法使用平方强度之和的基于梯度的最小化，将渲染图像中的特征与真实图像中的特征配准。为了改善视觉算法的收敛性和收敛时间，IMU用于预测呈现的图像中定义兴趣区域（ROI）的特征的位置和可能的可变性;使用卡尔曼滤波器框架融合IMU的测量结果和上述每种位置和方向估计方法。在每种情况下都进行了数值模拟，以验证配方的正确性。最后，在宾夕法尼亚州运输学院（PTI）的测试跑道设施上进行了实验，以针对高精度GPS / IMU系统测试每种方法的性能。

著录项

  • 作者

    Gupta, Vishisht.;

  • 作者单位

    The Pennsylvania State University.;

  • 授予单位 The Pennsylvania State University.;
  • 学科 Engineering Automotive.;Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 229 p.
  • 总页数 229
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术及设备;机械、仪表工业;
  • 关键词

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