Abstract
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.
@inproceedings {LIM-CVPR12,
author = "Hyon Lim and Sudipta N. Sinha and
Michael F. Cohen and Matthew Uyttendaele",
title = "Real-time Image-based 6-DOF Localization in Large-Scale Environments",
booktitle = "IEEE Computer Society Conference on Computer
Vision and Pattern Recognition (CVPR 2012)",
location = "Providence, RI",
month = "June",
year = "2012",
}
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