We present a real-time approach for image-based localization within large
scenes that have been reconstructed offline using structure from motion (Sfm).
From monocular video, our method continuously computes a precise 6-DOF camera
pose, by efficiently tracking natural features and matching them to 3D points
in the Sfm point cloud. Our main contribution lies in efficiently interleaving
a fast keypoint tracker that uses inexpensive binary feature descriptors with a
new approach for direct 2D-to-3D matching. The 2D-to-3D matching avoids the
need for online extraction of scale-invariant features. Instead, offline we
construct an indexed database containing multiple DAISY descriptors per 3D
point extracted at multiple scales. The key to the efficiency of our method
lies in invoking DAISY descriptor extraction and matching sparingly during
localization, and in distributing this computation over a window of successive
frames. This enables the algorithm to run in real-time, without fluctuations in
the latency over long durations. We evaluate the method in large indoor and
outdoor scenes. Our algorithm runs at over 30 Hz on a laptop and at 12 Hz on a
low-power, mobile computer suitable for onboard computation on a quadrotor
micro aerial vehicle.

该研究提出了一种基于图像的实时定位方法，通过结合一种基于二进制特征描述符的快速关键点跟踪方法和一个新的直接 2D 到 3D 匹配方法，有效地对大场景下的相机位置进行计算，并实现了实时的运行效果。