Fast Robust Large-scale Mapping from Video and Internet Photo ...

as the GPS/INS reported that the vehicle moved up 10 cm in one example.
4.2. Global Correspondences
Since our system creates the camera registration sequentially from the
input frames the obtained registrations are always subject to drift. Each
small inaccuracy in motion estimation will propagate forward and the absolute
positions and motions will be inaccurate. It is therefore necessary to
do a global optimization step afterwards to remove the drift. This makes
constraints necessary that are capable to remove drift. Such constraints can
come from global pose measurements like GPS, but more interesting is to
exploit internal consistencies like loops and intersections of the camera path.
In this section we will therefore discuss solutions to the challenging task of
detecting loops and intersections and using them for global optimization.
Registering the camera with respect to the previously estimated path
provides an estimate of the accumulated drift error. For robustness the path
self-intersection itself can only rely on the views itself and not on the estimated
camera motion, which can drift unbounded. Our method determines
the path intersection by evaluating the similarity of salient image features in
the current frame to all features in all previous views. We found that the
SIFT-feature [58] provides a good salient feature for our purposes. For the
fast computation of SIFT features, we make use our SiftGPU 2 , which can
for example extract SIFT features at 16Hz from 1024 × 768 images on an
NVidia GTX280. To enhance the robustness for the video streams we could
use local geometry (described in Section 5) to employ our view invariant
2 Available online: http://cs.unc.edu/∼ccwu/siftgpu/
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