增强现实中的跟踪技术

增强现实是一种正在发展的新技术，它将由计算机产生的虚拟场景或信息准确叠加到真实环境中，可以增强用户对真实世界的感知与交互能力；对用户视场及视点的跟踪是增强现实中实现虚、实场景配准的关键技术之一；通过对增强现实中跟踪技术性能要求的讨论，介绍了基于磁场、声学、惯性、光学传感等多种跟踪技术，分析了各种方法的跟踪性能与局限性，认为以基于视觉跟踪为主的混合跟踪技术将是增强现实系统的主流跟踪技术，重点论述了基于视觉的跟踪与基于视觉一惯性的混合跟踪技术及其有待解决的问题，并针对户外增强现实中的跟踪技术做了专门讨论。     

紧耦合多传感器混合跟踪算法

在增强现实应用中实现对运动目标的准确跟踪是一个具有挑战性的任务。基于混合跟踪通过对多传感器信息的融合通常比单一传感器跟踪算法更为优越的特性,提出了一种新的紧耦合混合跟踪算法实现视觉与惯性传感器信息的实时融合。该算法基于多频率的测量数据同步,通过强跟踪滤波器引入时变衰减因子自适应调整滤波预测误差协方差,实现对运动目标位置数据的准确估计。通过标示物被遮挡状态下的跟踪实验结果表明,该方法能有效改善基于扩展卡尔曼滤波器的混合跟踪算法对运动目标位置信息预测估计的准确性,提高跟踪快速移动目标的稳定性,适用于大范围移动条件下的增强现实系统。     

适用于户外增强现实系统的混合跟踪定位算法

单一传感器无法解决户外增强现实系统中的跟踪定位问题.为了提高视觉跟踪定位算法的精度和鲁棒性,提出一种基于惯性跟踪器与视觉测量相结合的混合跟踪定位算法.该算法在扩展卡尔曼滤波框架下,通过融合来自视觉与惯性传感器的信息进行摄像机运动轨迹估计,并利用视觉测量信息对惯性传感器的零点偏差进行实时校正;同时采用SCAAT方法解决惯性传感器与视觉测量间的时间采样不同步问题.实验结果表明,该算法能够有效地提高运动估计的精度和稳定性.     

基于混合注册方式的海洋环境增强现实系统

针对单一传感器无法解决海洋环境增强现实系统中跟踪注册的问题,提出融合智能手机AGPS、电子罗盘和加速度传感器测定视线方向的多传感器注册与基于海天线特征标识的视觉跟踪注册的混合注册方法,并通过扩展卡尔曼滤波器实现多传感器有效融合以提高三维注册精度。以真实海洋环境及海洋数值预报为例,提出适应海洋动态环境的增强现实框架,利用Vuforia AR实现了原型系统,并对混合注册方法进行了验证。结果表明,该技术框架及注册方法有较强的可用性和实用性,在海洋动态环境的增强现实中具有广泛应用前景。     

增强现实中的混合跟踪算法研究

单一的跟踪方法存在较大的局限性,为提高增强现实中跟踪环节的实时性和准确性,针对视觉跟踪和磁力跟踪的特点进行研究,提出了一种基于自适应粒子滤波的混合跟踪算法,用于对头部运动轨迹估计。该算法通过分析系统状态,自适应地融合多传感器数据,并建立相应的状态转移模型和系统量测模型;另外,该算法能在非线性非高斯的环境下动态地改变滤波器的粒子数和噪声方差,最终实现对头部运动轨迹的实时、正确估计。实验结果表明,该算法能有效地提高基于视觉和磁的混合跟踪的鲁棒性和运动估计的准确性。     

增强现实混合跟踪技术综述

混合跟踪是增强现实(Augmented reality, AR)领域最近二十年发展迅速的重要关键技术, 是增强现实姿态跟踪系统同时实现高精度和强鲁棒性的有效途径. 本文全面完整地论述了增强现实混合跟踪技术,详细阐述了混合跟踪器所涉及的混合跟踪、标定和时间同步等重要方法, 介绍了不同类型混合跟踪器的当前应用现状,然后探讨了混合跟踪技术的发展趋势及其存在的难题,最后展望了混合跟踪技术的应用前景.     

基于混合现实交互界面的下颌运动仿真系统

针对传统医学可视化仿真系统中交互界面和交互技术的缺点,基于视觉跟踪技术采用实物模型设计和实现了一个基于混合现实界面的下颌运动仿真系统。通过对物理界面元素、虚拟界面元素以及交互隐喻的分析,为混合现实环境下的交互技术建立了一个具有通用性的分类体系。物理与虚拟的界面元素之间通过语义映射将用户的操作从物理世界映射到虚拟空间中,从而为用户提供一种更为直观、自然的交互方式。     

增强现实系统及其关键技术研究

首先概要介绍了增强现实技术的概念、相关特性,指出了视觉一致性是增强现实系统最需要解决的问题;接着给出了增强现实系统的总体结构,概略介绍了各组成部分的功能以及对应的关键技术;然后在此基础上论述了关键技术中的显示技术和跟踪注册技术,并以跟踪注册为重点介绍了的增强现实系统基于跟踪器的方法、视觉跟踪方法和复合跟踪法等多种方案,并分析了不同方法不同实现的特性和应用场合,最后对增强现实的当前应用和未来趋势做出了概要总结.     

基于混合跟踪的增强现实系统设计与实现

为了提高增强现实系统在可变环境中的跟踪性能,设计了一种基于混合跟踪的增强现实系统,可根据使用者所处状态与环境动态地加载不同传感器的数据流,调用相应的跟踪算法,进行数据融合,得到准确的空间位置信息,并对系统硬件资源实现动态管理,具备一定的可扩展性和可移植性.最后给出了应用实例及分析.     

增强现实中三维跟踪注册技术概述

三维跟踪注册技术是增强现实领域中的重要关键技术，通过对现实场景中的图像或物体进行跟踪与定位，将虚拟物体按照正确的空间透视关系叠加到真实场景当中。综述了增强现实中的三维跟踪注册技术（基于计算机视觉的跟踪注册技术、基于硬件传感器的跟踪注册技术以及混合跟踪注册技术），详细阐述了不同跟踪注册技术的优势与不足，并介绍了不同跟踪注册技术的当前应用现状，随后探讨了跟踪注册技术的发展趋势与存在的难题，对三维跟踪注册技术的进一步研究进行了展望。     

The effect of tracking technique on the quality of user experience for augmented reality mobile navigation

Augmented reality (AR) technology is changing the way we interact with the world by making the journey seamless and interactive. This is done by layering digital enhancements over an existing reality or real life scenario. However, employing AR technologies in wayfinding and navigation does not automatically bring positive experiences. We argue that tracking techniques are important in mobile AR navigation and fundamentally affect the quality of user experience. In this paper, we propose two different tourist AR applications based on different tracking techniques. In addition to the analysis of the applications, we conducted a user evaluation to study the effect of different AR tracking techniques on the performance of users and the quality of user experience.     

A performance study for camera pose estimation using visual marker based tracking

Vision-based tracking systems are widely used for augmented reality (AR) applications. Their registration can be very accurate and there is no delay between real and virtual scene. However, vision-based tracking often suffers from limited range, errors, heavy processing time and present erroneous behavior due to numerical instability. To address these shortcomings, robust method are required to overcome these problems. In this paper, we survey classic vision-based pose computations and present a method that offers increased robustness and accuracy in the context of real-time AR tracking. In this work, we aim to determine the performance of four pose estimation methods in term of errors and execution time. We developed a hybrid approach that mixes an iterative method based on the extended Kalman filter (EKF) and an analytical method with direct resolution of pose parameters computation. The direct method initializes the pose parameters of the EKF algorithm which performs an optimization of these parameters thereafter. An evaluation of the pose estimation methods was obtained using a series of tests and an experimental protocol. The analysis of results shows that our hybrid algorithm improves stability, convergence and accuracy of the pose parameters.     

Hybrid tracking for outdoor augmented reality applications

We've developed a fully mobile, wearable AR system that combines a vision-based tracker (primarily software algorithms) that uses natural landmarks, with an inertial tracker (custom hardware and firmware) based on silicon micromachined accelerometers and gyroscopes. Unlike other vision-based and hybrid systems, both components recover the full 6 DOF pose. Fusing the two tracking subsystems gives us the benefits of both technologies, while the sensors' complementary nature helps overcome sensor-specific deficiencies. Our system is tailored to affordable, lightweight, energy-efficient mobile AR applications for urban environments, especially the historic centers of European cities.     

Tracking in unprepared environments for augmented reality systems

Many augmented reality applications require accurate tracking. Existing tracking techniques require prepared environments to ensure accurate results. This paper motivates the need to pursue augmented reality tracking techniques that work in unprepared environments, where users are not allowed to modify the real environment, such as in outdoor applications. Accurate tracking in such situations is difficult, requiring hybrid approaches. This paper summarizes two 3DOF results: a real-time system with a compass — inertial hybrid, and a non-real-time system fusing optical and inertial inputs. We then describe the preliminary results of 5- and 6-DOF tracking methods run in simulation. Future work and limitations are described.     

An augmented reality interface for visualizing and interacting with virtual content

In this paper, a novel AR interface is proposed that provides generic solutions to the tasks involved in augmenting simultaneously different types of virtual information and processing of tracking data for natural interaction. Participants within the system can experience a real-time mixture of 3D objects, static video, images, textual information and 3D sound with the real environment. The user-friendly AR interface can achieve maximum interaction using simple but effective forms of collaboration based on the combinations of human–computer interaction techniques. To prove the feasibility of the interface, the use of indoor AR techniques are employed to construct innovative applications and demonstrate examples from heritage to learning systems. Finally, an initial evaluation of the AR interface including some initial results is presented.     

Efficient estimation of hybrid systems with applications to tracking

Particle filtering has been recognised as a superior alternative to the traditional estimation methods as it is applicable to nonlinear/non-Gaussian system. A central issue in real-time applications of particle filtering is its high computational cost. This problem is compounded when it is used in hybrid system estimation. A new particle filtering method for nonlinear/non-Gaussian hybrid system estimation is proposed in this article. The new method integrates the high-accuracy interacting multiple model particle filtering algorithm with the computationally efficient observation and transition-based most likely modes tracking particle filtering algorithm to get high-accuracy estimation with reduced computational load. The algorithm is applied to a manoeuvring target tracking application to demonstrate its efficiency.     

Multithreaded Hybrid Feature Tracking for Markerless Augmented Reality

We describe a novel markerless camera tracking approach and user interaction methodology for augmented reality (AR) on unprepared tabletop environments. We propose a real-time system architecture that combines two types of feature tracking. Distinctive image features of the scene are detected and tracked frame-to-frame by computing optical flow. In order to achieve real-time performance, multiple operations are processed in a synchronized multi-threaded manner: capturing a video frame, tracking features using optical flow, detecting distinctive invariant features, and rendering an output frame. We also introduce user interaction methodology for establishing a global coordinate system and for placing virtual objects in the AR environment by tracking a user's outstretched hand and estimating a camera pose relative to it. We evaluate the speed and accuracy of our hybrid feature tracking approach, and demonstrate a proof-of-concept application for enabling AR in unprepared tabletop environments, using bare hands for interaction.