视觉slam十四讲第七章课后习题7
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7、题目要求:在ICP程序中，将空间点也作为优化变量考虑进来，程序应该如何书写？最后结果会有何变化？
分析:在ICP例程中，本书使用的是自定义的一个继承BaseUnaryEdge的边，从例子中的EdgeProjectXYZRGBDPoseOnly这个类在linearizeOplus中写下了关于位姿节点的雅克比矩阵，里面也没有相机模型参数模型（没有涉及到相机内参），没有关于空间坐标点的雅克比矩阵。通过书上175页误差函数，可以将空间点也作为优化变量，可在这个函数内部加入关于空间点的雅克比矩阵-R,因为优化空间点时会与位姿节点构成关系，所以在图中我们将空间点和位姿节点链接起来，建立一个二元边，仿照g2o::EdgeProjectXYZ2UV这个类的写法。写下修改后的类：
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（）估计一个值后，然后用映射函数 就是旋转加平移 将其_point映射到另一个相机坐标系下去 13 //观测值 - 映射值 因为我们做的试验是3D-3D 所以这里把第一帧的3D坐标当做测量值，然后把第二帧坐标映射到第一帧坐标系中 14 _error = _measurement - pose->estimate().map( point->estimate() ); 15 } 16 //表示线性化 误差函数 就是要计算雅克比矩阵 17 virtual void linearizeOplus()override final //这里override 表示override覆盖基类的同名同参函数， final表示派生类的某个函数不能覆盖这个函数 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() );//映射到第二帧相机坐标系下的坐标值 23 double x = xyz_trans[0]; //第一帧到第二帧坐标系下变换后的坐标值 24 double y = xyz_trans[1]; 25 double z = xyz_trans[2]; 26 27 //关于空间点的雅克比矩阵-R 28 _jacobianOplusXi = -T.rotation().toRotationMatrix(); 29 30 //3x6的关于优化变量的雅克比矩阵 可以看书上p179页 自己推到的结果 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优化过程中我们假定仅仅优化第二帧的空间点坐标，因此在g2o::SparseOptimizer中我们加入空间点为待优化节点，对应节点部分添加如下代码：
1 #if MyselfOptimizerMethod //添加空间节点 并且按照书上的方式进行优化的 2 int pointIndex = 1; //因为上面的位姿节点就1个 所以我们这里标号为1 3 for( auto &p: pts2 ){ 4 auto point = new g2o::VertexSBAPointXYZ(); 5 point->setId( pointIndex++ ); 6 point->setMarginalized( true ); //设置边缘化(必须设置 否则出错) 7 point->setEstimate( Eigen::Vector3d( p.x, p.y, p.z ) ); 8 optimizer.addVertex( point ); 9 } 10 #endif
对应边的部分添加如下代码:
edge->setVertex( 0 , dynamic_cast< g2o::VertexSBAPointXYZ *> ( optimizer.vertex(index)) ); edge->setVertex( 1 , dynamic_cast< g2o::OptimizableGraph::Vertex *> ( optimizer.vertex(0) ) );
在BA函数最后加入优化前后第二帧点的位置信息,仅仅以前三个点对为例:
1 cout<<"输出优化后的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<<"优化前的点:"<<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码开头部分加入了一些配置信息的输出：
1 void printConfigInfo(){ 2 cout<<"This is the example from GaoXiang's book : ICP结果是第二帧到第一帧的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<<"优化了空间点和位姿节点！"<<endl; 14 #else 15 cout<<"未对空间点进行优化！"<<endl; 16 #endif 17 18 #endif 19 }
在最开始配置区部分，读者可以自己调节系统如何输出,配置区代码如下:
1 /*+++++++++++++++++++++++++++++++++++++++++配置信息+++++++++++++++++++++++++++++++++++++++++++++***/ 2 3 #define MyselfOptimizerMethod 1 // 1: 课后习题7代码结果 4 // 0: 3d-3d书上例子 5 #define ISBAProvideInitValue 0 // 1: 用ICP结果为BA提供初值 6 // 0: 用单位矩阵I和0平移向量作为初始值 7 8 #define BAOptimizer 1 // 1: 加入BA优化 9 // 0: 不加入BA优化 10 11 /*+++++++++++++++++++++++++++++++++++++++++End配置信息+++++++++++++++++++++++++++++++++++++++++++++*/
总体代码以及结果图如下:
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: 课后习题7代码结果 23 // 0: 3d-3d书上例子 24 #define ISBAProvideInitValue 0 // 1: 用ICP结果为BA提供初值 25 // 0: 用单位矩阵I和0平移向量作为初始值 26 27 #define BAOptimizer 1 // 1: 加入BA优化 28 // 0: 不加入BA优化 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 // 像素坐标转相机归一化坐标 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 //课后习题7题 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（）估计一个值后，然后用映射函数 就是旋转加平移 将其_point映射到另一个相机坐标系下去 67 //观测值 - 映射值 因为我们做的试验是3D-3D 所以这里把第一帧的3D坐标当做测量值，然后把第二帧坐标映射到第一帧坐标系中 68 _error = _measurement - pose->estimate().map( point->estimate() ); 69 } 70 //表示线性化 误差函数 就是要计算雅克比矩阵 71 virtual void linearizeOplus()override final //这里override 表示override覆盖基类的同名同参函数， final表示派生类的某个函数不能覆盖这个函数 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() );//映射到第二帧相机坐标系下的坐标值 77 double x = xyz_trans[0]; //第一帧到第二帧坐标系下变换后的坐标值 78 double y = xyz_trans[1]; 79 double z = xyz_trans[2]; 80 81 //关于空间点的雅克比矩阵-R 82 _jacobianOplusXi = -T.rotation().toRotationMatrix(); 83 84 //3x6的关于优化变量的雅克比矩阵 可以看书上p179页 自己推到的结果 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 边代表误差 所以在里面要override一个computerError函数 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（）估计一个值后，然后用映射函数 就是旋转加平移 将其_point映射到另一个相机坐标系下去 123 //观测值 - 映射值 因为我们做的试验是3D-3D 所以这里把第一帧的3D坐标当做测量值，然后把第二帧坐标映射到第一帧坐标系中 124 _error = _measurement - pose->estimate().map( _point ); 125 } 126 //表示线性化 误差函数 就是要计算雅克比矩阵 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);//映射到第二帧相机坐标系下的坐标值 132 double x = xyz_trans[0]; //第一帧到第二帧坐标系下变换后的坐标值 133 double y = xyz_trans[1]; 134 double z = xyz_trans[2]; 135 136 //3x6的关于优化变量的雅克比矩阵 可以看书上p179页 自己推到的结果 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; //设立误差点 之后将其与测量值进行相减 求得误差 163 }; 164 #endif 165 166 #endif 167 //自己设置的打印配置信息 168 void printConfigInfo(){ 169 cout<<"This is the example from GaoXiang's book : ICP结果是第二帧到第一帧的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<<"优化了空间点和位姿节点！"<<endl; 181 #else 182 cout<<"未对空间点进行优化！"<<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();//输出配置信息 195 //-- 读取图像 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() <<"组匹配点"<<endl; 203 204 // 建立3D点 205 Mat depth1 = imread ( argv[3], CV_LOAD_IMAGE_UNCHANGED ); // 深度图为16位无符号数，单通道图像 206 Mat depth2 = imread ( argv[4], CV_LOAD_IMAGE_UNCHANGED ); // 深度图为16位无符号数，单通道图像 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 //存储特征点的3D坐标 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方式的位姿估计 229 cout<<"ICP via SVD results: "<<endl; 230 cout<<"R = "<<R<<endl; 231 cout<<"t = "<<t<<endl; 232 //实际上是第一帧到第二帧的R T 233 cout<<"第一帧到第二帧的旋转和平移:" << 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优化估计相机位姿 RT 是提供优化的初始值 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:检测 Oriented FAST 角点位置 270 detector->detect ( img_1,keypoints_1 ); 271 detector->detect ( img_2,keypoints_2 ); 272 273 //-- 第二步:根据角点位置计算 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:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离 278 vector<DMatch> match; 279 // BFMatcher matcher ( NORM_HAMMING ); 280 matcher->match ( descriptors_1, descriptors_2, match ); 281 282 //-- 第四步:匹配点对筛选 283 double min_dist=10000, max_dist=0; 284 285 //找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离 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 //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限. 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 去质心坐标 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 );//因为知道矩阵W的类型 所以在分解的时候直接是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 //例程本身的实现方式 求出的R T是第二帧到第一帧的 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 ) //实际上 R t在这里并不是必要的 这个只是用来提供BA初始值 369 { 370 // 初始化g2o 371 typedef g2o::BlockSolver< g2o::BlockSolverTraits<6,3> > Block; // pose维度为 6, landmark 维度为 3 372 std::unique_ptr<Block::LinearSolverType>linearSolver = g2o::make_unique<g2o::LinearSolverEigen<Block::PoseMatrixType>>(); //线性方程求解器 373 std::unique_ptr<Block>solver_ptr = g2o::make_unique<Block>( std::move(linearSolver) ); //矩阵块求解器 374 g2o::OptimizationAlgorithmLevenberg * solver = new g2o::OptimizationAlgorithmLevenberg( std::move(solver_ptr) ); //LM法 375 376 /* //另一种修改错误的方式 377 Block::LinearSolverType* linearSolver = new g2o::LinearSolverEigen<Block::PoseMatrixType>(); // 线性方程求解器 378 Block* solver_ptr = new Block( std::unique_ptr<Block::LinearSolverType>(linearSolver) ); // 矩阵块求解器 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 这里仅仅添加了第二帧节点 385 auto pose = new g2o::VertexSE3Expmap(); // camera pose 386 pose->setId(0); // 位姿节点编号为0 387 #if ISBAProvideInitValue // 为图优化提供ICP结果作为初值 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 // 提供默认初值 399 pose->setEstimate( g2o::SE3Quat( 400 Eigen::Matrix3d::Identity(), 401 Eigen::Vector3d( 0,0,0 ) 402 ) ); 403 #endif 404 optimizer.addVertex( pose ); //向优化器中添加节点 405 #if MyselfOptimizerMethod //添加空间节点 并且按照书上的方式进行优化的 406 int pointIndex = 1; //因为上面的位姿节点就1个 所以我们这里标号为1 407 for( auto &p: pts2 ){ 408 auto point = new g2o::VertexSBAPointXYZ(); 409 point->setId( pointIndex++ ); 410 point->setMarginalized( true ); //设置边缘化(必须设置 否则出错) 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( ); //在课后习题中修改的EdgeProjectXYZRGBDPoseOnly可以去掉_point成员变量 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 //参考ORB_SLAM:这里将原来的g2o::VertexSE3Expmap* 替换为g2o::OptimizableGraph::Vertex * 431 edge->setVertex( 0 , dynamic_cast< g2o::OptimizableGraph::Vertex *> ( optimizer.vertex(0) ) ); 432 #endif 433 //这里以第一帧为测量值 说明优化的是第二帧到第一帧的旋转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); //向优化器中添加边 439 index++; 440 edges.push_back(edge); //存储边指针 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<<"输出优化后的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<<"优化前的点:"<<endl; 459 for (int i = 0; i <3 ; ++i) { 460 cout<<pts2[i]<<endl<<endl; 461 } 462 */ 463 464 } 465 #endif
运行结果截图:
参考资料:
视觉slam十四讲从理论到实践
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