Autonomous estimation of the shape of the landscape
In an earlier post, I asserted that it was possible to get a precise map from photographs that only required a translation and rotation. These operations are not enough. You also need a scale change. This is the well-known relative orientation problem in photogrammetry.
However, the conclusion still remains that it is operationally possible to precisely estimate the shape of the terrain without knowing anything about the actual locations of your imaging sensor. If you couple the photographs with a GPS receiver signal you would then narrow considerably the scale change needed to overlay your constructed map with the actual shape of the landscape. In other words, a lot of GPS measurements would get you close to the true scale needed to overlay the map. Let me explain
One can well imagine a noisy GPS receiver that makes you think that two locations of the aerial camera are closer than they actually are. This would result in the constructed map being at a smaller scale than the real world. Likewise, the GPS readings could make you think that the cameras were farther apart when the photographs were taken. This would inflate your map scale. But in either case, the correct scale would be close to the inferred scale.
Now imagine that you consider more and more photographs with their corresponding GPS reading. Since GPS readings are unbiased (one of their great virtues), it would be extremely unlikely in a probabilistic sense that your inferred scale would be far from the real world scale.
Users can take your map and easily overlay it on another map by performing three operations: translation, rotation, and a small scale change. This is an extremely easy thing to do in comparison to trying to get absolute accuracy from the get go!
