视觉里程计3（SLAM十四讲ch7）-PnP
PnP 3D2D
PnP问题
- PnP为
Perspective-n-Point的简称，是求解3D到2D点对的运动的方法:即给出n个3D空间点及其投影位置时，如何求解相机的位姿。 - 典型的PnP问题求解方式有很多种，例如P3P，
直接线性变换(DLT),EPnP(Efficient PnP), UPnP。还有非线性的Bundle Adjustment.
直接线性变换(DLT)
P3P
BA
实践
使用OpenCV中的EPnP求解PnP问题，然后通过g2o对结果进行优化。使用RGB-D中的深度图作为特征点的3D位置。
在得到配对特征点后，在第一个图的深度图中寻找他们的深度，并求出空间位置。以此空间位置为3D点，再以第二个图的像素位置为2D点，调用EPnP求解PnP问题。
pose_estimation_3d2d
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
using namespace std;
using namespace cv;
void find_feature_matches (
const Mat& img_1, const Mat& img_2,
std::vector& keypoints_1,
std::vector& keypoints_2,
std::vector< DMatch >& matches );
// 像素坐标转相机归一化坐标
Point2d pixel2cam ( const Point2d& p, const Mat& K );
void bundleAdjustment (
const vector points_3d,
const vector points_2d,
const Mat& K,
Mat& R, Mat& t
);
int main ( int argc, char** argv )
{
if ( argc != 5 )
{
cout<<"usage: pose_estimation_3d2d img1 img2 depth1 depth2"< keypoints_1, keypoints_2;
vector matches;
find_feature_matches ( img_1, img_2, keypoints_1, keypoints_2, matches );
cout<<"一共找到了"< ( 3,3 ) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1 );
vector pts_3d;
vector pts_2d;
for ( DMatch m:matches )
{
ushort d = d1.ptr (int ( keypoints_1[m.queryIdx].pt.y )) [ int ( keypoints_1[m.queryIdx].pt.x ) ];
if ( d == 0 ) // bad depth
continue;
float dd = d/5000.0;
Point2d p1 = pixel2cam ( keypoints_1[m.queryIdx].pt, K );
pts_3d.push_back ( Point3f ( p1.x*dd, p1.y*dd, dd ) );
pts_2d.push_back ( keypoints_2[m.trainIdx].pt );
}
cout<<"3d-2d pairs: "<& keypoints_1,
std::vector& keypoints_2,
std::vector< DMatch >& matches )
{
//-- 初始化
Mat descriptors_1, descriptors_2;
// used in OpenCV3
Ptr detector = ORB::create();
Ptr descriptor = ORB::create();
// use this if you are in OpenCV2
// Ptr detector = FeatureDetector::create ( "ORB" );
// Ptr descriptor = DescriptorExtractor::create ( "ORB" );
Ptr matcher = DescriptorMatcher::create ( "BruteForce-Hamming" );
//-- 第一步:检测 Oriented FAST 角点位置
detector->detect ( img_1,keypoints_1 );
detector->detect ( img_2,keypoints_2 );
//-- 第二步:根据角点位置计算 BRIEF 描述子
descriptor->compute ( img_1, keypoints_1, descriptors_1 );
descriptor->compute ( img_2, keypoints_2, descriptors_2 );
//-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
vector match;
// BFMatcher matcher ( NORM_HAMMING );
matcher->match ( descriptors_1, descriptors_2, match );
//-- 第四步:匹配点对筛选
double min_dist=10000, max_dist=0;
//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
for ( int i = 0; i < descriptors_1.rows; i++ )
{
double dist = match[i].distance;
if ( dist < min_dist ) min_dist = dist;
if ( dist > max_dist ) max_dist = dist;
}
printf ( "-- Max dist : %f \n", max_dist );
printf ( "-- Min dist : %f \n", min_dist );
//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.
for ( int i = 0; i < descriptors_1.rows; i++ )
{
if ( match[i].distance <= max ( 2*min_dist, 30.0 ) )
{
matches.push_back ( match[i] );
}
}
}
Point2d pixel2cam ( const Point2d& p, const Mat& K )
{
return Point2d
(
( p.x - K.at ( 0,2 ) ) / K.at ( 0,0 ),
( p.y - K.at ( 1,2 ) ) / K.at ( 1,1 )
);
}
void bundleAdjustment (
const vector< Point3f > points_3d,
const vector< Point2f > points_2d,
const Mat& K,
Mat& R, Mat& t )
{
// 初始化g2o
typedef g2o::BlockSolver< g2o::BlockSolverTraits<6,3> > Block; // pose 维度为 6, landmark 维度为 3
Block::LinearSolverType* linearSolver = new g2o::LinearSolverCSparse(); // 线性方程求解器
Block* solver_ptr = new Block ( linearSolver ); // 矩阵块求解器
g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( solver_ptr );
g2o::SparseOptimizer optimizer;
optimizer.setAlgorithm ( solver );
// vertex
g2o::VertexSE3Expmap* pose = new g2o::VertexSE3Expmap(); // camera pose
Eigen::Matrix3d R_mat;
R_mat <<
R.at ( 0,0 ), R.at ( 0,1 ), R.at ( 0,2 ),
R.at ( 1,0 ), R.at ( 1,1 ), R.at ( 1,2 ),
R.at ( 2,0 ), R.at ( 2,1 ), R.at ( 2,2 );
pose->setId ( 0 );
pose->setEstimate ( g2o::SE3Quat (
R_mat,
Eigen::Vector3d ( t.at ( 0,0 ), t.at ( 1,0 ), t.at ( 2,0 ) )
) );
optimizer.addVertex ( pose );
int index = 1;
for ( const Point3f p:points_3d ) // landmarks
{
g2o::VertexSBAPointXYZ* point = new g2o::VertexSBAPointXYZ();
point->setId ( index++ );
point->setEstimate ( Eigen::Vector3d ( p.x, p.y, p.z ) );
point->setMarginalized ( true ); // g2o 中必须设置 marg 参见第十讲内容
optimizer.addVertex ( point );
}
// parameter: camera intrinsics
g2o::CameraParameters* camera = new g2o::CameraParameters (
K.at ( 0,0 ), Eigen::Vector2d ( K.at ( 0,2 ), K.at ( 1,2 ) ), 0
);
camera->setId ( 0 );
optimizer.addParameter ( camera );
// edges
index = 1;
for ( const Point2f p:points_2d )
{
g2o::EdgeProjectXYZ2UV* edge = new g2o::EdgeProjectXYZ2UV();
edge->setId ( index );
edge->setVertex ( 0, dynamic_cast ( optimizer.vertex ( index ) ) );
edge->setVertex ( 1, pose );
edge->setMeasurement ( Eigen::Vector2d ( p.x, p.y ) );
edge->setParameterId ( 0,0 );
edge->setInformation ( Eigen::Matrix2d::Identity() );
optimizer.addEdge ( edge );
index++;
}
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
optimizer.setVerbose ( true );
optimizer.initializeOptimization();
optimizer.optimize ( 100 );
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration time_used = chrono::duration_cast> ( t2-t1 );
cout<<"optimization costs time: "<
