Filling the gaps: Hybrid vision and inertial tracking

Ky Waegel, Frederick P. Brooks
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引用次数: 5

Abstract

Existing head-tracking systems all suffer from various limitations, such as latency, cost, accuracy, or drift. I propose to address these limitations by using depth cameras and existing 3D reconstruction algorithms to simultaneously localize the camera position and build a map of the environment, providing stable and drift-free tracking. This method is enabled by the recent proliferation of light-weight, inexpensive depth cameras. Because these cameras have a relatively slow frame rate, I combine this technique with a low-latency inertial measurement unit to estimate movement between frames. Using the generated environment model, I further propose a collision avoidance system for use with real walking.
填补空白:混合视觉和惯性跟踪
现有的头部跟踪系统都有各种各样的限制,比如延迟、成本、准确性或漂移。我建议通过使用深度相机和现有的3D重建算法来解决这些限制,同时定位相机位置并构建环境地图,提供稳定和无漂移的跟踪。这种方法是由于最近大量出现的轻质、廉价的深度相机而实现的。由于这些相机具有相对较慢的帧速率,因此我将这种技术与低延迟惯性测量单元结合起来,以估计帧之间的移动。利用生成的环境模型,我进一步提出了一个用于真实行走的碰撞避免系统。
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