1、使用PCL工具
1 //創建一個模型參數對象，用於記錄結果 2 pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients); 3 //inliers表示誤差能容忍的點，記錄點雲序號 4 pcl::PointIndices::Ptr inliers(new pcl::PointIndices); 5 //創建一個分割器 6 pcl::SACSegmentation<pcl::PointXYZ> seg; 7 //Optional,設置結果平面展示的點是分割掉的點還是分割剩下的點 8 seg.setOptimizeCoefficients(true); 9 //Mandatory-設置目標幾何形狀 10 seg.setModelType(pcl::SACMODEL_PLANE); 11 //分割方法：隨機采樣法 12 seg.setMethodType(pcl::SAC_RANSAC); 13 //設置誤差容忍范圍，也就是閾值 14 seg.setDistanceThreshold(0.01); 15 //輸入點雲 16 seg.setInputCloud (cloud); 17 //分割點雲 18 seg.segment (*inliers, *coefficients);
2、RANSAC擬合平面代碼
while ((iterNum < iter_maxNum) && inPlaneNum_max <= RSample_pointsNum) { inPlaneNum_t = 3;//當前擬合平面中點個數 //隨機抽3個點，驗證不在一條直線上 /*A(x1,y1)、B(x2,y2)、C(x3,y3) AB斜率：kAB=(y2-y1)/(x2-x1) BC斜率：kBC=(y3-y2)/(x3-x2) 計算結果可得：kAB=kBC 因為kAB=kBC，且共點B 所以直線AB與直線BC共線。*/ do { rand_i_1 = real(gen); rand_i_2 = real(gen); if (rand_i_1 == rand_i_2)continue; rand_i_3 = real(gen); if (rand_i_1 == rand_i_3 || rand_i_2 == rand_i_3)continue; x1 = r_sample[rand_i_1].x; x2 = r_sample[rand_i_2].x; x3 = r_sample[rand_i_3].x; y1 = r_sample[rand_i_1].y; y2 = r_sample[rand_i_2].y; y3 = r_sample[rand_i_3].y; } while (((y2 - y1)*(x3 - x2)) == ((y3 - y2)*(x2 - x1))); //x1 = r_sample[rand_i_1].x; x2 = r_sample[rand_i_2].x; x3 = r_sample[rand_i_3].x; //y1 = r_sample[rand_i_1].y; y2 = r_sample[rand_i_2].y; y3 = r_sample[rand_i_3].y; z1 = r_sample[rand_i_1].z; z2 = r_sample[rand_i_2].z; z3 = r_sample[rand_i_3].z; //求平面方程 A_t = (y2 - y1)*(z3 - z1) - (z2 - z1)*(y3 - y1); B_t = (x3 - x1)*(z2 - z1) - (x2 - x1)*(z3 - z1); C_t = (x2 - x1)*(y3 - y1) - (x3 - x1)*(y2 - y1); D_t = -(A_t * x1 + B_t * y1 + C_t * z1); //求在平面內的點的個數 temp = sqrt(A_t*A_t + B_t*B_t + C_t*C_t);//點到平面距離參數 for (int i = 0; i < RSample_pointsNum; i++) { temp_D = abs(A_t*r_sample[i].x + B_t*r_sample[i].y + C_t*r_sample[i].z + D_t) / temp;//點到平面距離 if (temp_D < maxD) { inPlaneNum_t++; } } //與最優（最大）個數比較，保留最優個數的平面公式 if (inPlaneNum_t > inPlaneNum_max) { A_best = A_t; B_best = B_t; C_best = C_t; D_best = D_t; inPlaneNum_max = inPlaneNum_t; } iterNum++;//迭代次數+1 }
3、多點情況迭代次數的計算（轉載於https://www.cnblogs.com/littlepear/p/10129861.html）
4、關於RANSAC算法https://blog.csdn.net/weixin_43795395/article/details/90751650講得很好，可以參考
5、自適應閾值https://blog.csdn.net/hanshuobest/article/details/73718440