GPS Refinement and Camera Orientation Estimation from a Single Image and a 2D Map
Improve GPS accuracy by a building photo and a 2D map in urban environments.
Example: GPS tells me a location, but with several meters inaccuracy. If I also have a photo and the map,
I know only at certain positions could I see a building looks like that, then I can probably find out where
am I more accurately.
Step 1: Find vertical building edges in the photo, represent them in a tilt-invariant way: Tilt-Invariant Vertical Edge
Position (TICEP) features.
Step 2: We don't know (and we can't know for sure) which building the camera looks at. So for each building, compute
Location-Orientation Hypotheses (LOHs) as if it is the building in the photo.
Example: all LOHs (blue), and the correct one (red):
Step 3: Remove unreasonable LOHs, then select the one nearest to the noisy GPS reading. And that's our final result!
Note: not guaranteed to 100% find better position than GPS, but generally speaking achieves better accuracy.
Example: The correct LOH (much more accurate than GPS as geometry doesn't lie!) can be selected as long as GPS falls
into the red area (the refinable area).
 Hang Chu, Andrew Gallagher, and Tsuhan Chen, GPS Refinement and Camera Orientation Estimation from a Single Image and a 2D Map, Workshop on Mobile Vision, IEEE Computer Vision and Pattern Recognition (CVPR), 2014. [pdf]