ransac算法_pcl库(1)-RANSAC方法滤除地面

Given:
    data - a set of observed data points(这里指点云数据)
    model - a model that can be fitted to data points(这里指平面模型)
    n - the minimum number of data values required to fit the model
    (n - 满足模型的最少数据个数)
    k - the maximum number of iterations allowed in the algorithm(最多迭代次数)
    t - a threshold value for determining when a data point fits a model
    (t - 点是否适用于模型的阈值，这里指的是点到平面的距离)
    d - the number of close data values required to assert that a model fits well to data
(   (d - 判定模型是否适用于数据集的数据数目)
Return:
    bestfit - model parameters which best fit the data (or nil if no good model is found)
 
iterations = 0
bestfit = nil
besterr = something really large
while iterations < k {
    maybeinliers = n randomly selected values from data
    maybemodel = model parameters fitted to maybeinliers
    alsoinliers = empty set
    for every point in data not in maybeinliers {
        if point fits maybemodel with an error smaller than t
             add point to alsoinliers
    }
    if the number of elements in alsoinliers is > d {
        % this implies that we may have found a good model
        % now test how good it is
        bettermodel = model parameters fitted to all points in maybeinliers and alsoinliers
        thiserr = a measure of how well model fits these points
        if thiserr < besterr {
            bestfit = bettermodel
            besterr = thiserr
        }
    }
    increment iterations
}
return bestfit

        seg.setDistanceThreshold(0.2);