Robust Estimator

Parameter Estimator

class Param_Estimator

This class defines the interface for all parameter estimators. Classes inherit from this interface can be used by the Ransac class to perform robust estimation. This class include three methods:

  • estimate(): estimation of parameters using the minimum number of data (exact estimation);
  • ls_estimate(): estimation of parameters using over-determined data to minimize a least squares cost function;
  • check_inlier(): check if the data fits the estimated model.
virtual void estimate(std::vector<T>& data, std::vector<S>& params) = 0;

pure virtual method that estimate parameters using minimum mumber of data.

virtual void ls_estimate(std::vector<T>& data, std::vector<S>& params) = 0;

pure virtual method that estimate parameters using over-determined data that minimizes a least squares cost function.

virtual int check_inlier(T& data, std::vector<S>& params) = 0;

pure virtual method that check if data fits the estimated parameters.

RANSAC

class Ransac

This class implements the classic RANSAC framework proposed by [Martin] et al.

template<class T, class S>
class Ransac
{
public:
    // - params:            a vector containing the estimated parameters
    // - param_estimator:   an instance which can estimate the desired parameters by either an exact
    //                      fit or a least squares fit
    // - data:              the input from which the parameters will be estimated
    // - prob_wo_outlieres: the probability that at least one of the selected subsets doens't contain an outlier,
    //                      must be in (0, 1).
    //
    // - number of iterations (k):
    //
    //               log(1 - p)
    //         k = --------------
    //              log(1 - w^n)
    //
    // - p: desired probability without an outlier
    // - w: percentage of inliers in the data
    // - n: minimum number of data for estimation
    //
    static float estimate(Param_Estimator<T, S>* param_estimator,
                          std::vector<T>& data,
                          std::vector<S>& params,
                          float prob_wo_outliers,
                          int* vote_inlier);
}