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7、題目要求:在ICP程序中，將空間點也作為優化變量考慮進來，程序應該如何書寫？最后結果會有何變化？
分析:在ICP例程中，本書使用的是自定義的一個繼承BaseUnaryEdge的邊，從例子中的EdgeProjectXYZRGBDPoseOnly這個類在linearizeOplus中寫下了關於位姿節點的雅克比矩陣，里面也沒有相機模型參數模型（沒有涉及到相機內參），沒有關於空間坐標點的雅克比矩陣。通過書上175頁誤差函數，可以將空間點也作為優化變量，可在這個函數內部加入關於空間點的雅克比矩陣-R,因為優化空間點時會與位姿節點構成關系，所以在圖中我們將空間點和位姿節點鏈接起來，建立一個二元邊，仿照g2o::EdgeProjectXYZ2UV這個類的寫法。寫下修改后的類：
1 class EdgeProjectXYZRGBDPoseOnly : public g2o::BaseBinaryEdge<3, Eigen::Vector3d,g2o::VertexSBAPointXYZ, g2o::VertexSE3Expmap> 2 { 3 public: 4 EIGEN_MAKE_ALIGNED_OPERATOR_NEW; 5 EdgeProjectXYZRGBDPoseOnly( ) {} 6 7 virtual void computeError() 8 { 9 const g2o::VertexSBAPointXYZ * point = dynamic_cast< const g2o::VertexSBAPointXYZ *> ( _vertices[0] ); 10 const g2o::VertexSE3Expmap * pose = dynamic_cast<const g2o::VertexSE3Expmap*> ( _vertices[1] ); 11 // measurement is p, point is p' 12 //pose->estimate().map( _point );中用estimate（）估計一個值后，然后用映射函數 就是旋轉加平移 將其_point映射到另一個相機坐標系下去 13 //觀測值 - 映射值 因為我們做的試驗是3D-3D 所以這里把第一幀的3D坐標當做測量值，然后把第二幀坐標映射到第一幀坐標系中 14 _error = _measurement - pose->estimate().map( point->estimate() ); 15 } 16 //表示線性化 誤差函數 就是要計算雅克比矩陣 17 virtual void linearizeOplus()override final //這里override 表示override覆蓋基類的同名同參函數， final表示派生類的某個函數不能覆蓋這個函數 18 { 19 g2o::VertexSE3Expmap* pose = dynamic_cast<g2o::VertexSE3Expmap *> (_vertices[1]); 20 g2o::VertexSBAPointXYZ * point = dynamic_cast< g2o::VertexSBAPointXYZ * > (_vertices[0] ); 21 g2o::SE3Quat T(pose->estimate()); 22 Eigen::Vector3d xyz_trans = T.map( point->estimate() );//映射到第二幀相機坐標系下的坐標值 23 double x = xyz_trans[0]; //第一幀到第二幀坐標系下變換后的坐標值 24 double y = xyz_trans[1]; 25 double z = xyz_trans[2]; 26 27 //關於空間點的雅克比矩陣-R 28 _jacobianOplusXi = -T.rotation().toRotationMatrix(); 29 30 //3x6的關於優化變量的雅克比矩陣 可以看書上p179頁 自己推到的結果 31 _jacobianOplusXj(0,0) = 0; 32 _jacobianOplusXj(0,1) = -z; 33 _jacobianOplusXj(0,2) = y; 34 _jacobianOplusXj(0,3) = -1; 35 _jacobianOplusXj(0,4) = 0; 36 _jacobianOplusXj(0,5) = 0; 37 38 _jacobianOplusXj(1,0) = z; 39 _jacobianOplusXj(1,1) = 0; 40 _jacobianOplusXj(1,2) = -x; 41 _jacobianOplusXj(1,3) = 0; 42 _jacobianOplusXj(1,4) = -1; 43 _jacobianOplusXj(1,5) = 0; 44 45 _jacobianOplusXj(2,0) = -y; 46 _jacobianOplusXj(2,1) = x; 47 _jacobianOplusXj(2,2) = 0; 48 _jacobianOplusXj(2,3) = 0; 49 _jacobianOplusXj(2,4) = 0; 50 _jacobianOplusXj(2,5) = -1; 51 } 52 53 bool read ( istream& in ) {} 54 bool write ( ostream& out ) const {} 55 };
在BA優化過程中我們假定僅僅優化第二幀的空間點坐標，因此在g2o::SparseOptimizer中我們加入空間點為待優化節點，對應節點部分添加如下代碼：
1 #if MyselfOptimizerMethod //添加空間節點 並且按照書上的方式進行優化的 2 int pointIndex = 1; //因為上面的位姿節點就1個 所以我們這里標號為1 3 for( auto &p: pts2 ){ 4 auto point = new g2o::VertexSBAPointXYZ(); 5 point->setId( pointIndex++ ); 6 point->setMarginalized( true ); //設置邊緣化(必須設置 否則出錯) 7 point->setEstimate( Eigen::Vector3d( p.x, p.y, p.z ) ); 8 optimizer.addVertex( point ); 9 } 10 #endif
對應邊的部分添加如下代碼:
edge->setVertex( 0 , dynamic_cast< g2o::VertexSBAPointXYZ *> ( optimizer.vertex(index)) ); edge->setVertex( 1 , dynamic_cast< g2o::OptimizableGraph::Vertex *> ( optimizer.vertex(0) ) );
在BA函數最后加入優化前后第二幀點的位置信息,僅僅以前三個點對為例:
1 cout<<"輸出優化后的point值:"<<endl; 2 for (int j = 0; j <3 ; ++j) { 3 cout<< dynamic_cast< g2o::VertexSBAPointXYZ * > ( optimizer.vertex(j+1) )->estimate()<<endl<<endl;//Eigen::Vector3d 4 } 5 cout<<endl<<"優化前的點:"<<endl; 6 for (int i = 0; i <3 ; ++i) { 7 cout<<pts2[i]<<endl<<endl; 8 }
需要注意的是自己在代碼開頭部分加入了一些配置信息的輸出：
1 void printConfigInfo(){ 2 cout<<"This is the example from GaoXiang's book : ICP結果是第二幀到第一幀的RT."<<endl; 3 #if BAOptimizer 4 5 cout<<"BA Optimizer is Provided!"<<endl; 6 #if ISBAProvideInitValue 7 cout<<"The RT from ICP's result is Provided for BA as a initialization."<<endl; 8 #else 9 cout<<" But,Not provide initialization for BA!"<<endl; 10 #endif 11 12 #if MyselfOptimizerMethod 13 cout<<"優化了空間點和位姿節點！"<<endl; 14 #else 15 cout<<"未對空間點進行優化！"<<endl; 16 #endif 17 18 #endif 19 }
在最開始配置區部分，讀者可以自己調節系統如何輸出,配置區代碼如下:
1 /*+++++++++++++++++++++++++++++++++++++++++配置信息+++++++++++++++++++++++++++++++++++++++++++++***/ 2 3 #define MyselfOptimizerMethod 1 // 1: 課后習題7代碼結果 4 // 0: 3d-3d書上例子 5 #define ISBAProvideInitValue 0 // 1: 用ICP結果為BA提供初值 6 // 0: 用單位矩陣I和0平移向量作為初始值 7 8 #define BAOptimizer 1 // 1: 加入BA優化 9 // 0: 不加入BA優化 10 11 /*+++++++++++++++++++++++++++++++++++++++++End配置信息+++++++++++++++++++++++++++++++++++++++++++++*/
總體代碼以及結果圖如下:
1 #include <iostream> 2 #include <opencv2/core/core.hpp> 3 #include <opencv2/features2d/features2d.hpp> 4 #include <opencv2/highgui/highgui.hpp> 5 #include <opencv2/calib3d/calib3d.hpp> 6 #include <Eigen/Core> 7 #include <Eigen/Geometry> 8 #include <Eigen/SVD> 9 #include <g2o/core/base_vertex.h> 10 #include <g2o/core/base_unary_edge.h> 11 #include <g2o/core/block_solver.h> 12 #include <g2o/core/optimization_algorithm_gauss_newton.h> 13 #include <g2o/solvers/eigen/linear_solver_eigen.h> 14 #include <g2o/types/sba/types_six_dof_expmap.h> 15 #include <chrono> 16 #include <g2o/core/optimization_algorithm_levenberg.h> 17 18 using namespace std; 19 using namespace cv; 20 /*+++++++++++++++++++++++++++++++++++++++++配置信息+++++++++++++++++++++++++++++++++++++++++++++***/ 21 22 #define MyselfOptimizerMethod 1 // 1: 課后習題7代碼結果 23 // 0: 3d-3d書上例子 24 #define ISBAProvideInitValue 0 // 1: 用ICP結果為BA提供初值 25 // 0: 用單位矩陣I和0平移向量作為初始值 26 27 #define BAOptimizer 1 // 1: 加入BA優化 28 // 0: 不加入BA優化 29 30 /*+++++++++++++++++++++++++++++++++++++++++End配置信息+++++++++++++++++++++++++++++++++++++++++++++*/ 31 32 void find_feature_matches ( 33 const Mat& img_1, const Mat& img_2, 34 std::vector<KeyPoint>& keypoints_1, 35 std::vector<KeyPoint>& keypoints_2, 36 std::vector< DMatch >& matches ); 37 38 // 像素坐標轉相機歸一化坐標 39 Point2d pixel2cam ( const Point2d& p, const Mat& K ); 40 41 void pose_estimation_3d3d ( 42 const vector<Point3f>& pts1, 43 const vector<Point3f>& pts2, 44 Mat& R, Mat& t 45 ); 46 47 #if BAOptimizer 48 void bundleAdjustment ( 49 const vector< Point3f >& pts1, 50 const vector< Point3f >& pts2, 51 Mat& R, Mat& t ); 52 53 #if MyselfOptimizerMethod 54 //課后習題7題 55 class EdgeProjectXYZRGBDPoseOnly : public g2o::BaseBinaryEdge<3, Eigen::Vector3d,g2o::VertexSBAPointXYZ, g2o::VertexSE3Expmap> 56 { 57 public: 58 EIGEN_MAKE_ALIGNED_OPERATOR_NEW; 59 EdgeProjectXYZRGBDPoseOnly( ) {} 60 61 virtual void computeError() 62 { 63 const g2o::VertexSBAPointXYZ * point = dynamic_cast< const g2o::VertexSBAPointXYZ *> ( _vertices[0] ); 64 const g2o::VertexSE3Expmap * pose = dynamic_cast<const g2o::VertexSE3Expmap*> ( _vertices[1] ); 65 // measurement is p, point is p' 66 //pose->estimate().map( _point );中用estimate（）估計一個值后，然后用映射函數 就是旋轉加平移 將其_point映射到另一個相機坐標系下去 67 //觀測值 - 映射值 因為我們做的試驗是3D-3D 所以這里把第一幀的3D坐標當做測量值，然后把第二幀坐標映射到第一幀坐標系中 68 _error = _measurement - pose->estimate().map( point->estimate() ); 69 } 70 //表示線性化 誤差函數 就是要計算雅克比矩陣 71 virtual void linearizeOplus()override final //這里override 表示override覆蓋基類的同名同參函數， final表示派生類的某個函數不能覆蓋這個函數 72 { 73 g2o::VertexSE3Expmap* pose = dynamic_cast<g2o::VertexSE3Expmap *> (_vertices[1]); 74 g2o::VertexSBAPointXYZ * point = dynamic_cast< g2o::VertexSBAPointXYZ * > (_vertices[0] ); 75 g2o::SE3Quat T(pose->estimate()); 76 Eigen::Vector3d xyz_trans = T.map( point->estimate() );//映射到第二幀相機坐標系下的坐標值 77 double x = xyz_trans[0]; //第一幀到第二幀坐標系下變換后的坐標值 78 double y = xyz_trans[1]; 79 double z = xyz_trans[2]; 80 81 //關於空間點的雅克比矩陣-R 82 _jacobianOplusXi = -T.rotation().toRotationMatrix(); 83 84 //3x6的關於優化變量的雅克比矩陣 可以看書上p179頁 自己推到的結果 85 _jacobianOplusXj(0,0) = 0; 86 _jacobianOplusXj(0,1) = -z; 87 _jacobianOplusXj(0,2) = y; 88 _jacobianOplusXj(0,3) = -1; 89 _jacobianOplusXj(0,4) = 0; 90 _jacobianOplusXj(0,5) = 0; 91 92 _jacobianOplusXj(1,0) = z; 93 _jacobianOplusXj(1,1) = 0; 94 _jacobianOplusXj(1,2) = -x; 95 _jacobianOplusXj(1,3) = 0; 96 _jacobianOplusXj(1,4) = -1; 97 _jacobianOplusXj(1,5) = 0; 98 99 _jacobianOplusXj(2,0) = -y; 100 _jacobianOplusXj(2,1) = x; 101 _jacobianOplusXj(2,2) = 0; 102 _jacobianOplusXj(2,3) = 0; 103 _jacobianOplusXj(2,4) = 0; 104 _jacobianOplusXj(2,5) = -1; 105 } 106 107 bool read ( istream& in ) {} 108 bool write ( ostream& out ) const {} 109 }; 110 #else 111 // g2o edge 邊代表誤差 所以在里面要override一個computerError函數 112 class EdgeProjectXYZRGBDPoseOnly : public g2o::BaseUnaryEdge<3, Eigen::Vector3d, g2o::VertexSE3Expmap> 113 { 114 public: 115 EIGEN_MAKE_ALIGNED_OPERATOR_NEW; 116 EdgeProjectXYZRGBDPoseOnly( const Eigen::Vector3d& point ) : _point(point) {} 117 118 virtual void computeError() 119 { 120 const g2o::VertexSE3Expmap* pose = dynamic_cast<const g2o::VertexSE3Expmap*> ( _vertices[0] ); 121 // measurement is p, point is p' 122 //pose->estimate().map( _point );中用estimate（）估計一個值后，然后用映射函數 就是旋轉加平移 將其_point映射到另一個相機坐標系下去 123 //觀測值 - 映射值 因為我們做的試驗是3D-3D 所以這里把第一幀的3D坐標當做測量值，然后把第二幀坐標映射到第一幀坐標系中 124 _error = _measurement - pose->estimate().map( _point ); 125 } 126 //表示線性化 誤差函數 就是要計算雅克比矩陣 127 virtual void linearizeOplus() 128 { 129 g2o::VertexSE3Expmap* pose = dynamic_cast<g2o::VertexSE3Expmap *>(_vertices[0]); 130 g2o::SE3Quat T(pose->estimate()); 131 Eigen::Vector3d xyz_trans = T.map(_point);//映射到第二幀相機坐標系下的坐標值 132 double x = xyz_trans[0]; //第一幀到第二幀坐標系下變換后的坐標值 133 double y = xyz_trans[1]; 134 double z = xyz_trans[2]; 135 136 //3x6的關於優化變量的雅克比矩陣 可以看書上p179頁 自己推到的結果 137 _jacobianOplusXi(0,0) = 0; 138 _jacobianOplusXi(0,1) = -z; 139 _jacobianOplusXi(0,2) = y; 140 _jacobianOplusXi(0,3) = -1; 141 _jacobianOplusXi(0,4) = 0; 142 _jacobianOplusXi(0,5) = 0; 143 144 _jacobianOplusXi(1,0) = z; 145 _jacobianOplusXi(1,1) = 0; 146 _jacobianOplusXi(1,2) = -x; 147 _jacobianOplusXi(1,3) = 0; 148 _jacobianOplusXi(1,4) = -1; 149 _jacobianOplusXi(1,5) = 0; 150 151 _jacobianOplusXi(2,0) = -y; 152 _jacobianOplusXi(2,1) = x; 153 _jacobianOplusXi(2,2) = 0; 154 _jacobianOplusXi(2,3) = 0; 155 _jacobianOplusXi(2,4) = 0; 156 _jacobianOplusXi(2,5) = -1; 157 } 158 159 bool read ( istream& in ) {} 160 bool write ( ostream& out ) const {} 161 protected: 162 Eigen::Vector3d _point; //設立誤差點 之后將其與測量值進行相減 求得誤差 163 }; 164 #endif 165 166 #endif 167 //自己設置的打印配置信息 168 void printConfigInfo(){ 169 cout<<"This is the example from GaoXiang's book : ICP結果是第二幀到第一幀的RT."<<endl; 170 #if BAOptimizer 171 172 cout<<"BA Optimizer is Provided!"<<endl; 173 #if ISBAProvideInitValue 174 cout<<"The RT from ICP's result is Provided for BA as a initialization."<<endl; 175 #else 176 cout<<" But,Not provide initialization for BA!"<<endl; 177 #endif 178 179 #if MyselfOptimizerMethod 180 cout<<"優化了空間點和位姿節點！"<<endl; 181 #else 182 cout<<"未對空間點進行優化！"<<endl; 183 #endif 184 185 #endif 186 } 187 int main ( int argc, char** argv ) 188 { 189 if ( argc != 5 ) 190 { 191 cout<<"usage: pose_estimation_3d3d img1 img2 depth1 depth2"<<endl; 192 return 1; 193 } 194 printConfigInfo();//輸出配置信息 195 //-- 讀取圖像 196 Mat img_1 = imread ( argv[1], CV_LOAD_IMAGE_COLOR ); 197 Mat img_2 = imread ( argv[2], CV_LOAD_IMAGE_COLOR ); 198 199 vector<KeyPoint> keypoints_1, keypoints_2; 200 vector<DMatch> matches; 201 find_feature_matches ( img_1, img_2, keypoints_1, keypoints_2, matches ); 202 cout<<"一共找到了"<<matches.size() <<"組匹配點"<<endl; 203 204 // 建立3D點 205 Mat depth1 = imread ( argv[3], CV_LOAD_IMAGE_UNCHANGED ); // 深度圖為16位無符號數，單通道圖像 206 Mat depth2 = imread ( argv[4], CV_LOAD_IMAGE_UNCHANGED ); // 深度圖為16位無符號數，單通道圖像 207 Mat K = ( Mat_<double> ( 3,3 ) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1 ); 208 vector<Point3f> pts1, pts2; 209 210 for ( DMatch m:matches ) 211 { 212 ushort d1 = depth1.ptr<unsigned short> ( int ( keypoints_1[m.queryIdx].pt.y ) ) [ int ( keypoints_1[m.queryIdx].pt.x ) ]; 213 ushort d2 = depth2.ptr<unsigned short> ( int ( keypoints_2[m.trainIdx].pt.y ) ) [ int ( keypoints_2[m.trainIdx].pt.x ) ]; 214 if ( d1==0 || d2==0 ) // bad depth 215 continue; 216 Point2d p1 = pixel2cam ( keypoints_1[m.queryIdx].pt, K ); 217 Point2d p2 = pixel2cam ( keypoints_2[m.trainIdx].pt, K ); 218 float dd1 = float ( d1 ) /5000.0; 219 float dd2 = float ( d2 ) /5000.0; 220 221 //存儲特征點的3D坐標 222 pts1.push_back ( Point3f ( p1.x*dd1, p1.y*dd1, dd1 ) ); 223 pts2.push_back ( Point3f ( p2.x*dd2, p2.y*dd2, dd2 ) ); 224 } 225 226 cout<<"3d-3d pairs: "<<pts1.size() <<endl; 227 Mat R, t; 228 pose_estimation_3d3d ( pts1, pts2, R, t ); //ICP方式的位姿估計 229 cout<<"ICP via SVD results: "<<endl; 230 cout<<"R = "<<R<<endl; 231 cout<<"t = "<<t<<endl; 232 //實際上是第一幀到第二幀的R T 233 cout<<"第一幀到第二幀的旋轉和平移:" << endl << "R_inv = "<<R.t() <<endl; 234 cout<<"t_inv = "<<-R.t() *t<<endl; 235 236 cout<<"calling bundle adjustment"<<endl; 237 238 #if BAOptimizer 239 bundleAdjustment( pts1, pts2, R, t ); //BA優化估計相機位姿 RT 是提供優化的初始值 240 #endif 241 242 // verify p1 = R*p2 + t 243 /* for ( int i=0; i<5; i++ ) 244 { 245 cout<<"p1 = "<<pts1[i]<<endl; 246 cout<<"p2 = "<<pts2[i]<<endl; 247 cout<<"(R*p2+t) = "<< 248 R * (Mat_<double>(3,1)<<pts2[i].x, pts2[i].y, pts2[i].z) + t 249 <<endl; 250 cout<<endl; 251 } 252 */ 253 } 254 255 void find_feature_matches ( const Mat& img_1, const Mat& img_2, 256 std::vector<KeyPoint>& keypoints_1, 257 std::vector<KeyPoint>& keypoints_2, 258 std::vector< DMatch >& matches ) 259 { 260 //-- 初始化 261 Mat descriptors_1, descriptors_2; 262 // used in OpenCV3 263 Ptr<FeatureDetector> detector = ORB::create(); 264 Ptr<DescriptorExtractor> descriptor = ORB::create(); 265 // use this if you are in OpenCV2 266 // Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" ); 267 // Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" ); 268 Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming"); 269 //-- 第一步:檢測 Oriented FAST 角點位置 270 detector->detect ( img_1,keypoints_1 ); 271 detector->detect ( img_2,keypoints_2 ); 272 273 //-- 第二步:根據角點位置計算 BRIEF 描述子 274 descriptor->compute ( img_1, keypoints_1, descriptors_1 ); 275 descriptor->compute ( img_2, keypoints_2, descriptors_2 ); 276 277 //-- 第三步:對兩幅圖像中的BRIEF描述子進行匹配，使用 Hamming 距離 278 vector<DMatch> match; 279 // BFMatcher matcher ( NORM_HAMMING ); 280 matcher->match ( descriptors_1, descriptors_2, match ); 281 282 //-- 第四步:匹配點對篩選 283 double min_dist=10000, max_dist=0; 284 285 //找出所有匹配之間的最小距離和最大距離, 即是最相似的和最不相似的兩組點之間的距離 286 for ( int i = 0; i < descriptors_1.rows; i++ ) 287 { 288 double dist = match[i].distance; 289 if ( dist < min_dist ) min_dist = dist; 290 if ( dist > max_dist ) max_dist = dist; 291 } 292 293 printf ( "-- Max dist : %f \n", max_dist ); 294 printf ( "-- Min dist : %f \n", min_dist ); 295 296 //當描述子之間的距離大於兩倍的最小距離時,即認為匹配有誤.但有時候最小距離會非常小,設置一個經驗值30作為下限. 297 for ( int i = 0; i < descriptors_1.rows; i++ ) 298 { 299 if ( match[i].distance <= max ( 2*min_dist, 30.0 ) ) 300 { 301 matches.push_back ( match[i] ); 302 } 303 } 304 } 305 306 Point2d pixel2cam ( const Point2d& p, const Mat& K ) 307 { 308 return Point2d 309 ( 310 ( p.x - K.at<double> ( 0,2 ) ) / K.at<double> ( 0,0 ), 311 ( p.y - K.at<double> ( 1,2 ) ) / K.at<double> ( 1,1 ) 312 ); 313 } 314 315 void pose_estimation_3d3d ( 316 const vector<Point3f>& pts1, 317 const vector<Point3f>& pts2, 318 Mat& R, Mat& t 319 ) 320 { 321 Point3f p1, p2; // center of mass 322 int N = pts1.size(); 323 for ( int i=0; i<N; i++ ) 324 { 325 p1 += pts1[i]; 326 p2 += pts2[i]; 327 } 328 p1 = Point3f( Vec3f(p1) / N); 329 p2 = Point3f( Vec3f(p2) / N); 330 vector<Point3f> q1 ( N ), q2 ( N ); // remove the center 去質心坐標 331 for ( int i=0; i<N; i++ ) 332 { 333 q1[i] = pts1[i] - p1; 334 q2[i] = pts2[i] - p2; 335 } 336 337 // compute q1*q2^T 338 Eigen::Matrix3d W = Eigen::Matrix3d::Zero(); 339 for ( int i=0; i<N; i++ ) 340 { 341 W += Eigen::Vector3d ( q1[i].x, q1[i].y, q1[i].z ) * Eigen::Vector3d ( q2[i].x, q2[i].y, q2[i].z ).transpose(); 342 } 343 cout<<"W="<<W<<endl; 344 345 // SVD on W 346 Eigen::JacobiSVD<Eigen::Matrix3d> svd ( W, Eigen::ComputeFullU|Eigen::ComputeFullV );//因為知道矩陣W的類型 所以在分解的時候直接是FullU | FullV 347 Eigen::Matrix3d U = svd.matrixU(); 348 Eigen::Matrix3d V = svd.matrixV(); 349 cout<<"U="<<U<<endl; 350 cout<<"V="<<V<<endl; 351 352 //例程本身的實現方式 求出的R T是第二幀到第一幀的 353 Eigen::Matrix3d R_ = U* ( V.transpose() ); 354 Eigen::Vector3d t_ = Eigen::Vector3d ( p1.x, p1.y, p1.z ) - R_ * Eigen::Vector3d ( p2.x, p2.y, p2.z ); 355 356 // convert to cv::Mat 357 R = ( Mat_<double> ( 3,3 ) << 358 R_ ( 0,0 ), R_ ( 0,1 ), R_ ( 0,2 ), 359 R_ ( 1,0 ), R_ ( 1,1 ), R_ ( 1,2 ), 360 R_ ( 2,0 ), R_ ( 2,1 ), R_ ( 2,2 ) 361 ); 362 t = ( Mat_<double> ( 3,1 ) << t_ ( 0,0 ), t_ ( 1,0 ), t_ ( 2,0 ) ); 363 } 364 #if BAOptimizer 365 void bundleAdjustment ( 366 const vector< Point3f >& pts1, 367 const vector< Point3f >& pts2, 368 Mat& R, Mat& t ) //實際上 R t在這里並不是必要的 這個只是用來提供BA初始值 369 { 370 // 初始化g2o 371 typedef g2o::BlockSolver< g2o::BlockSolverTraits<6,3> > Block; // pose維度為 6, landmark 維度為 3 372 std::unique_ptr<Block::LinearSolverType>linearSolver = g2o::make_unique<g2o::LinearSolverEigen<Block::PoseMatrixType>>(); //線性方程求解器 373 std::unique_ptr<Block>solver_ptr = g2o::make_unique<Block>( std::move(linearSolver) ); //矩陣塊求解器 374 g2o::OptimizationAlgorithmLevenberg * solver = new g2o::OptimizationAlgorithmLevenberg( std::move(solver_ptr) ); //LM法 375 376 /* //另一種修改錯誤的方式 377 Block::LinearSolverType* linearSolver = new g2o::LinearSolverEigen<Block::PoseMatrixType>(); // 線性方程求解器 378 Block* solver_ptr = new Block( std::unique_ptr<Block::LinearSolverType>(linearSolver) ); // 矩陣塊求解器 379 g2o::OptimizationAlgorithmLevenberg * solver = new g2o::OptimizationAlgorithmLevenberg ( std::unique_ptr <g2o::Solver>(solver_ptr) ); //LM法 380 */ 381 g2o::SparseOptimizer optimizer; 382 optimizer.setAlgorithm( solver ); 383 384 // vertex 這里僅僅添加了第二幀節點 385 auto pose = new g2o::VertexSE3Expmap(); // camera pose 386 pose->setId(0); // 位姿節點編號為0 387 #if ISBAProvideInitValue // 為圖優化提供ICP結果作為初值 388 Eigen::Matrix3d R_mat; 389 R_mat << 390 R.at<double> ( 0,0 ), R.at<double> ( 0,1 ), R.at<double> ( 0,2 ), 391 R.at<double> ( 1,0 ), R.at<double> ( 1,1 ), R.at<double> ( 1,2 ), 392 R.at<double> ( 2,0 ), R.at<double> ( 2,1 ), R.at<double> ( 2,2 ); 393 pose->setEstimate( g2o::SE3Quat( 394 R_mat, 395 Eigen::Vector3d( t.at<double> ( 0,0 ),t.at<double> ( 0,1 ),t.at<double> ( 0,2 ) ) 396 ) 397 ); 398 #else // 提供默認初值 399 pose->setEstimate( g2o::SE3Quat( 400 Eigen::Matrix3d::Identity(), 401 Eigen::Vector3d( 0,0,0 ) 402 ) ); 403 #endif 404 optimizer.addVertex( pose ); //向優化器中添加節點 405 #if MyselfOptimizerMethod //添加空間節點 並且按照書上的方式進行優化的 406 int pointIndex = 1; //因為上面的位姿節點就1個 所以我們這里標號為1 407 for( auto &p: pts2 ){ 408 auto point = new g2o::VertexSBAPointXYZ(); 409 point->setId( pointIndex++ ); 410 point->setMarginalized( true ); //設置邊緣化(必須設置 否則出錯) 411 point->setEstimate( Eigen::Vector3d( p.x, p.y, p.z ) ); 412 optimizer.addVertex( point ); 413 } 414 #endif 415 // edges 416 int index = 0; 417 vector<EdgeProjectXYZRGBDPoseOnly*> edges; 418 for ( size_t i=0; i<pts1.size(); i++ ) 419 { 420 #if MyselfOptimizerMethod 421 auto edge = new EdgeProjectXYZRGBDPoseOnly( ); //在課后習題中修改的EdgeProjectXYZRGBDPoseOnly可以去掉_point成員變量 422 #else 423 auto edge = new EdgeProjectXYZRGBDPoseOnly( 424 Eigen::Vector3d(pts2[i].x, pts2[i].y, pts2[i].z) ); 425 #endif 426 edge->setId( index ); 427 #if MyselfOptimizerMethod 428 edge->setVertex( 0 , dynamic_cast< g2o::VertexSBAPointXYZ *> ( optimizer.vertex(index)) ); 429 edge->setVertex( 1 , dynamic_cast< g2o::OptimizableGraph::Vertex *> ( optimizer.vertex(0) ) ); 430 #else //參考ORB_SLAM:這里將原來的g2o::VertexSE3Expmap* 替換為g2o::OptimizableGraph::Vertex * 431 edge->setVertex( 0 , dynamic_cast< g2o::OptimizableGraph::Vertex *> ( optimizer.vertex(0) ) ); 432 #endif 433 //這里以第一幀為測量值 說明優化的是第二幀到第一幀的旋轉r和平移t 434 edge->setMeasurement( Eigen::Vector3d ( 435 pts1[i].x, pts1[i].y, pts1[i].z) 436 ); 437 edge->setInformation( Eigen::Matrix3d::Identity()*1e4 ); 438 optimizer.addEdge(edge); //向優化器中添加邊 439 index++; 440 edges.push_back(edge); //存儲邊指針 441 } 442 443 chrono::steady_clock::time_point t1 = chrono::steady_clock::now(); 444 optimizer.setVerbose( true ); 445 optimizer.initializeOptimization(); 446 optimizer.optimize(100); 447 chrono::steady_clock::time_point t2 = chrono::steady_clock::now(); 448 chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2-t1); 449 cout<<"optimization costs time: "<<time_used.count()<<" seconds."<<endl; 450 451 cout<<endl<<"after optimization:"<<endl; 452 cout<<"T="<<endl<<Eigen::Isometry3d( pose->estimate() ).matrix()<<endl; 453 /* 454 cout<<"輸出優化后的point值:"<<endl; 455 for (int j = 0; j <3 ; ++j) { 456 cout<< dynamic_cast< g2o::VertexSBAPointXYZ * > ( optimizer.vertex(j+1) )->estimate()<<endl<<endl;//Eigen::Vector3d 457 } 458 cout<<endl<<"優化前的點:"<<endl; 459 for (int i = 0; i <3 ; ++i) { 460 cout<<pts2[i]<<endl<<endl; 461 } 462 */ 463 464 } 465 #endif
運行結果截圖:
參考資料:
視覺slam十四講從理論到實踐
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