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Google Releases Key Part of Street View Pipeline
New submitter drom writes "Google released a key part of their Street View pipeline as open source on Tuesday: Ceres Solver. It's a large-scale nonlinear least squares minimizer. What does that mean? It's a way to fit a model (like expected position of a car) to data (like GPS positions or accelerometers). The library is completely general and works for many problems. It offers state of the art performance for bundle adjustment problems typical in 3D reconstruction, among others."
The video shows you how they use it, not what it is. Parent is just trolling.
Take a curve, represented by some function with parameters.
Take a set of data points.
Fit the curve to the data points by changing the parameters.
Minimizing the error distances between data points and curve so you get a best fit.
It's easy enough for lines and polynomials, but for more general functions it becomes a real pain. The maths is straightforward, but the implementation can make a huge difference. Google's library looks like a well implemented minimizer and it's been road tested too.
This is good.
Least squares is also often used in parameter fitting. Suppose you have a simulation with an somehow unknown parameter p. First you use a first guess as a value of p. That you run your simulation and get observed values y_obs.
Now you compare the computed values y_obs with the measured values y_mes using squares: cost = (y_obs - y_mes)^2.
By computing d p / d cost you may adapt p and redo the above steps till the squares are minimized... thus a least squares problem.