Slashdot Mirror

← Back to Stories (view on slashdot.org)

Google Releases Key Part of Street View Pipeline

Posted by Soulskill on from the now-you-can-map-your-pillow-fort dept.

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."

6 of 44 comments (clear)

  1. Extra features by Canazza · · Score: 4, Funny

    Does it come with built-in Wi-Fi snooping?

    --
    It pays to be obvious, especially if you have a reputation for being subtle.
  2. Mind telling us what are the "Better Technology" ? by Taco+Cowboy · · Score: 5, Insightful

    Good effort I guess, but there is much better technology out there.

    Mind telling us what are the "Better Technology" ?

    Thank you !

    --
    Muchas Gracias, Señor Edward Snowden !
  3. Poor effort, there's much better trolls out there by Anonymous Coward · · Score: 5, Interesting

    Your comment suggests you don't know what at minimizer is, and I think you just wanted to say "Google is bad" to the first story related to Google.

    Which is a pity, but smarter trolls than you will have a go at them later.

    If this is a constrained minimizer then Libre Office and Open Office will suddenly get a major improvement in their solver functionality for example, because at the heart of a solver is a decent non linear minimizer.

    ----
    Fox News loves Bin Laden, hates President Obama for killing him.

  4. Ignore dvh by Anonymous Coward · · Score: 5, Informative

    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.

  5. Yeah... by IamTheRealMike · · Score: 5, Funny

    From the source code:

    // The reason why the Triggs approximation becomes so poor is
    // because the curvature correction that it applies to the gauss
    // newton hessian goes from being a full rank correction to a rank
    // deficient correction making the inversion of the Hessian fraught
    // with all sorts of misery and suffering.

    Yeah, that's pretty much how maths makes me feel as well ...

  6. Usefulness of least squares by Anonymous Coward · · Score: 4, Insightful

    When you have several measured points, and you want to fit a function to them, least squares is how you measure how closely your function fits. This is useful anytime you want to infer a trend into the future (extrapolation) or infer what values come between the measured values (interpolation).

    If you can speed up least-squares calculation, you can test your theories faster and possibly using less hardware - making that kind of statistical analysis more accessible.

    In Google's case, they are using it to adjust their raw GPS data so that they can get a more accurate idea of where they are.