EAGER: Viewpoint Tracking via Acceleration Stabilized with Computer Vision

EAGER：通过计算机视觉稳定的加速度进行视点跟踪

• 批准号: 1347208
• 负责人: Frederick Brooks, Jr
• 金额: $ 7.5万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1347208&HistoricalAwards=false
• 关键词: EAGER; Viewpoint; Tracking; via; Acceleration

This project addresses a problem which has kept virtual reality from widespread use. Some 15 years ago high-capability graphics cards in PCs reduced the cost of computing for virtual environments from hundreds-of-thousands of dollars to (today) hundreds of dollars. Low-cost head-mounted displays have just appeared. The similar advance in viewpoint tracking has not occurred; accurate, low-latency wide-area viewpoint tracking remains very costly. Virtual reality demands stereo 60 frames per second per eye and system latencies below 50 ms. This research is developing a novel system to provide accurate, low-latency viewpoint tracking to meet these requirements with consumer-cost components. The research is based upon a recently demonstrated proof-of-concept system. A standard RGB-Depth camera sits on the user's head. Pose is calculated by matching images against an environment model. A Kalman filter integrates rotational velocity and linear acceleration from a cheap high-speed inertial measurement unit (IMU) to update the pose estimate many times between frames. This not only gives low-latency pose readings, it also improves initial values for the next camera calculation. The depth images and reconstruction software are concurrently used to incrementally build/update the depth model of the environment for the camera matching. The current research is demonstrating the system's potential. To work completely successfully, both conceptual and algorithmic advances are in process. IMU calibrations are being improved. Temperature and dynamic bias must be compensated in the calibration to improve stimation and reduce jitter. Using multiple cameras to reduce overall noise and handle difficult cases (such as blank walls) are being addressed with new algorithms and evaluated. The merging of new and modeled data is computationally expensive. The feasibility demo uses two GPUs, one for rendering; one for tracking. Ways are being invented to do it on one. Additional future research includes tracking dynamic objects and incorporating object recognition (e.g., such as a desk or chairs) to improve estimates. Widespread access to virtual reality may well open new, unexpected creative uses of the technology. The research is inventing the proof-of-concept system forward to one that can make this exciting leap.

这个项目解决了一个阻碍虚拟现实广泛使用的问题。大约15年前，PC中的高性能图形卡将虚拟环境的计算成本从数十万美元降低到（今天）数百美元。低成本的头戴式显示器刚刚出现。视点跟踪方面的类似进展尚未出现;准确、低延迟的广域视点跟踪仍然非常昂贵。虚拟现实要求立体60帧每秒每只眼睛和系统延迟低于50毫秒。这项研究正在开发一种新的系统，提供准确的，低延迟的视点跟踪，以满足这些要求与消费者成本的组件。这项研究是基于最近展示的概念验证系统。一个标准的RGB深度相机位于用户的头上。通过将图像与环境模型进行匹配来计算姿势。卡尔曼滤波器集成来自廉价高速惯性测量单元（IMU）的旋转速度和线性加速度，以在帧之间多次更新姿态估计。这不仅提供了低延迟的姿势读数，还改进了下一个相机计算的初始值。深度图像和重建软件被同时用于递增地构建/更新用于相机匹配的环境的深度模型。目前的研究正在展示该系统的潜力。为了完全成功地工作，概念和算法的进步都在进行中。正在改进惯性测量装置的校准。在校准过程中，必须对温度和动态偏置进行补偿，以提高校准精度和减小抖动。使用多个摄像头来减少整体噪音和处理困难的情况（如空白的墙壁）正在通过新的算法来解决和评估。合并新数据和建模数据在计算上是昂贵的。可行性演示使用两个GPU，一个用于渲染，一个用于跟踪。人们正在发明一种方法来做到这一点。 其他未来的研究包括跟踪动态对象和结合对象识别（例如，例如桌子或椅子）以改进估计。虚拟现实的广泛使用很可能会开启这项技术新的、意想不到的创造性用途。该研究正在发明概念验证系统，以实现这一令人兴奋的飞跃。