8.3 实践：LK光流
（1）环境
opencv2
（2）代码详解
CMakeLists.txt
cmake_minimum_required( VERSION 2.8 )
project( useLK )
set( CMAKE_BUILD_TYPE Release )
set( CMAKE_CXX_FLAGS "-std=c++11 -O3" )
find_package( OpenCV )
include_directories( ${OpenCV_INCLUDE_DIRS} )
add_executable( useLK useLK.cpp )
target_link_libraries( useLK ${OpenCV_LIBS} )
useLK.cpp
#include <iostream>
#include <fstream>
#include <list>
#include <vector>
#include <chrono>
using namespace std;
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/video/tracking.hpp>
int main( int argc, char** argv )
{
if (argc != 2){ //如果参数数量不对
cout << "usage: useLK path_to_dataset" << endl;
return 1;
}
string path_to_dataset = argv[1]; //第一个参数
string associate_file = path_to_dataset + "/associate.txt";
ifstream fin(associate_file);
if (!fin) { //如果不存在这个路径
cerr << "I cann't find associate.txt!" << endl;
return 1;
}
string rgb_file, depth_file, time_rgb, time_depth;
list<cv::Point2f> keypoints; // 因为要删除跟踪失败的点，使用list
cv::Mat colorImage, depthImage, last_colorImage;
for (int index = 0; index < 100; index++){
fin >> time_rgb >> rgb_file >> time_depth >> depth_file;
colorImage = cv::imread(path_to_dataset + "/" + rgb_file);
depthImage = cv::imread(path_to_dataset + "/" + depth_file, -1);
if (index == 0){
// 对第一帧提取FAST特征点
vector<cv::KeyPoint> kps;
//cv::Ptr<cv::FastFeatureDetector> detector = cv::FastFeatureDetector::create();
cv::Ptr<cv::FeatureDetector> detector = cv::FeatureDetector::create ("ORB"); //opencv2 use this
detector->detect(colorImage, kps);
for (auto kp : kps) {
keypoints.push_back(kp.pt);
}
last_colorImage = colorImage;
continue;
}
if ( colorImage.data==nullptr || depthImage.data==nullptr ){
continue;
}
// 对其他帧用LK跟踪特征点
vector<cv::Point2f> next_keypoints;
vector<cv::Point2f> prev_keypoints;
for (auto kp : keypoints) {
prev_keypoints.push_back(kp);
}
vector<unsigned char> status;
vector<float> error;
//起始时间
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
cv::calcOpticalFlowPyrLK(last_colorImage, colorImage, prev_keypoints, next_keypoints, status, error);
//光流结束时间
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>( t2-t1 );
cout << "LK Flow use time：" << time_used.count() << " seconds." << endl;
// 把跟丢的点删掉
int i=0;
for (auto iter=keypoints.begin(); iter!=keypoints.end(); i++) {
if (status[i] == 0){
iter = keypoints.erase(iter);
continue;
}
*iter = next_keypoints[i];
iter++;
}
cout << "tracked keypoints: " << keypoints.size() << endl;
//如果全部跟丢
if (keypoints.size() == 0) {
cout << "all keypoints are lost." << endl;
break;
}
// 画出 keypoints
cv::Mat img_show = colorImage.clone();
for (auto kp : keypoints) {
cv::circle(img_show, kp, 12, cv::Scalar(0, 240, 0), 1);
}
cv::imshow("corners", img_show);
cv::waitKey(0);
last_colorImage = colorImage;
}
return 0;
}
（3）编译
data记得解压
mkdir build
cd build
cmake ..
make
cd ..
./build/useLK ../data
8.5 实践：RGB-D的直接法
8.5.1 稀疏直接法
（1）代码详解
CMakeLists.txt
cmake_minimum_required( VERSION 2.8 )
project( directMethod )
set( CMAKE_BUILD_TYPE Release )
set( CMAKE_CXX_FLAGS "-std=c++11 -O3" )
# 添加cmake模块路径
list( APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake_modules )
find_package( OpenCV )
include_directories( ${OpenCV_INCLUDE_DIRS} )
find_package( G2O )
include_directories( ${G2O_INCLUDE_DIRS} )
include_directories( "/usr/include/eigen3" )
set( G2O_LIBS
g2o_core g2o_types_sba g2o_solver_csparse g2o_stuff g2o_csparse_extension
)
add_executable( direct_sparse direct_sparse.cpp )
target_link_libraries( direct_sparse ${OpenCV_LIBS} ${G2O_LIBS} )
#add_executable( direct_semidense direct_semidense.cpp )
#target_link_libraries( direct_semidense ${OpenCV_LIBS} ${G2O_LIBS} )
directMethod.cpp
#include <iostream>
#include <fstream>
#include <list>
#include <vector>
#include <chrono>
#include <ctime>
#include <climits>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <g2o/core/base_unary_edge.h>
#include <g2o/core/block_solver.h>
#include <g2o/core/optimization_algorithm_levenberg.h>
#include <g2o/solvers/dense/linear_solver_dense.h>
#include <g2o/core/robust_kernel.h>
#include <g2o/types/sba/types_six_dof_expmap.h>
using namespace std;
using namespace g2o;
/********************************************
* 本节演示了RGBD上的稀疏直接法
********************************************/
// 一次测量的值，包括一个世界坐标系下三维点与一个灰度值
struct Measurement
{
Measurement (Eigen::Vector3d p, float g) : pos_world (p), grayscale (g) {}
Eigen::Vector3d pos_world;
float grayscale;
};
inline Eigen::Vector3d project2Dto3D ( int x, int y, int d, float fx, float fy, float cx, float cy, float scale )
{
float zz = float(d) / scale;
float xx = zz * (x - cx) / fx;
float yy = zz * (y - cy) / fy;
return Eigen::Vector3d ( xx, yy, zz );
}
inline Eigen::Vector2d project3Dto2D ( float x, float y, float z, float fx, float fy, float cx, float cy )
{
float u = fx*x/z + cx;
float v = fy*y/z + cy;
return Eigen::Vector2d ( u,v );
}
// 直接法估计位姿
// 输入：测量值（空间点的灰度），新的灰度图，相机内参； 输出：相机位姿
// 返回：true为成功，false失败
bool poseEstimationDirect ( const vector<Measurement>& measurements, cv::Mat* gray, Eigen::Matrix3f& intrinsics, Eigen::Isometry3d& Tcw );
// project a 3d point into an image plane, the error is photometric error
// an unary edge with one vertex SE3Expmap (the pose of camera)
//定义直接法的边
class EdgeSE3ProjectDirect: public BaseUnaryEdge< 1, double, VertexSE3Expmap>
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
EdgeSE3ProjectDirect() {}
EdgeSE3ProjectDirect ( Eigen::Vector3d point, float fx, float fy, float cx, float cy, cv::Mat* image )
: x_world_(point), fx_(fx), fy_(fy), cx_(cx), cy_(cy), image_(image) {}
virtual void computeError() {
const VertexSE3Expmap* v = static_cast<const VertexSE3Expmap*> (_vertices[0]);
Eigen::Vector3d x_local = v->estimate().map(x_world_);
float x = x_local[0]*fx_/x_local[2] + cx_;
float y = x_local[1]*fy_/x_local[2] + cy_;
// check x,y is in the image
if ( x-4<0 || (x+4)>image_->cols || (y-4)<0 || (y+4)>image_->rows ) {
_error ( 0,0 ) = 0.0;
this->setLevel ( 1 );
}
else {
_error ( 0,0 ) = getPixelValue ( x,y ) - _measurement;
}
}
// plus in manifold
virtual void linearizeOplus( ){
if ( level() == 1 ) {
_jacobianOplusXi = Eigen::Matrix<double, 1, 6>::Zero();
return;
}
VertexSE3Expmap* vtx = static_cast<VertexSE3Expmap*> ( _vertices[0] );
Eigen::Vector3d xyz_trans = vtx->estimate().map (x_world_); // q in book
double x = xyz_trans[0];
double y = xyz_trans[1];
double invz = 1.0/xyz_trans[2];
double invz_2 = invz*invz;
float u = x*fx_*invz + cx_;
float v = y*fy_*invz + cy_;
// jacobian from se3 to u,v
// NOTE that in g2o the Lie algebra is (\omega, \epsilon), where \omega is so(3) and \epsilon the translation
Eigen::Matrix<double, 2, 6> jacobian_uv_ksai;
jacobian_uv_ksai ( 0,0 ) = - x*y*invz_2 *fx_;
jacobian_uv_ksai ( 0,1 ) = ( 1+ ( x*x*invz_2 ) ) *fx_;
jacobian_uv_ksai ( 0,2 ) = - y*invz *fx_;
jacobian_uv_ksai ( 0,3 ) = invz *fx_;
jacobian_uv_ksai ( 0,4 ) = 0;
jacobian_uv_ksai ( 0,5 ) = -x*invz_2 *fx_;
jacobian_uv_ksai ( 1,0 ) = - ( 1+y*y*invz_2 ) *fy_;
jacobian_uv_ksai ( 1,1 ) = x*y*invz_2 *fy_;
jacobian_uv_ksai ( 1,2 ) = x*invz *fy_;
jacobian_uv_ksai ( 1,3 ) = 0;
jacobian_uv_ksai ( 1,4 ) = invz *fy_;
jacobian_uv_ksai ( 1,5 ) = -y*invz_2 *fy_;
Eigen::Matrix<double, 1, 2> jacobian_pixel_uv;
jacobian_pixel_uv(0,0) = (getPixelValue(u+1,v)-getPixelValue(u-1,v))/2;
jacobian_pixel_uv(0,1) = (getPixelValue(u,v+1)-getPixelValue(u,v-1))/2;
_jacobianOplusXi = jacobian_pixel_uv * jacobian_uv_ksai;
}
// dummy read and write functions because we don't care...
virtual bool read ( std::istream& in ) {}
virtual bool write ( std::ostream& out ) const {}
protected:
// get a gray scale value from reference image (bilinear interpolated)
inline float getPixelValue ( float x, float y ) {
uchar* data = & image_->data[int(y) * image_->step + int(x)];
float xx = x - floor ( x );
float yy = y - floor ( y );
return float (
(1-xx)*(1-yy)*data[0] + xx*(1-yy)*data[1] +
(1-xx)*yy*data[image_->step] + xx*yy*data[image_->step+1]);
}
public:
Eigen::Vector3d x_world_; // 3D point in world frame
float cx_=0, cy_=0, fx_=0, fy_=0; // Camera intrinsics
cv::Mat* image_=nullptr; // reference image
};
int main ( int argc, char** argv )
{
if (argc != 2){
cout << "usage: useLK path_to_dataset" << endl;
return 1;
}
srand ((unsigned int)time(0));
string path_to_dataset = argv[1];
string associate_file = path_to_dataset + "/associate.txt";
ifstream fin (associate_file);
string rgb_file, depth_file, time_rgb, time_depth;
cv::Mat color, depth, gray;
vector<Measurement> measurements;
// 相机内参
float cx = 325.5;
float cy = 253.5;
float fx = 518.0;
float fy = 519.0;
float depth_scale = 1000.0;
Eigen::Matrix3f K;
K << fx,0.f,cx,0.f,fy,cy,0.f,0.f,1.0f; //构造内参矩阵
Eigen::Isometry3d Tcw = Eigen::Isometry3d::Identity();
cv::Mat prev_color;
// 我们以第一个图像为参考，对后续图像和参考图像做直接法
for (int index = 0; index < 10; index++) {
cout << "*********** loop " << index << " ************" << endl;
fin >> time_rgb >> rgb_file >> time_depth >> depth_file;
color = cv::imread (path_to_dataset + "/" + rgb_file);
depth = cv::imread (path_to_dataset + "/" + depth_file, -1);
if ( color.data==nullptr || depth.data==nullptr )
continue;
cv::cvtColor (color, gray, cv::COLOR_BGR2GRAY);
if (index == 0) {
// 对第一帧提取FAST特征点
vector<cv::KeyPoint> keypoints;
//cv::Ptr<cv::FastFeatureDetector> detector = cv::FastFeatureDetector::create();
cv::Ptr<cv::FeatureDetector> detector = cv::FeatureDetector::create ("ORB"); //opencv2 use this
detector->detect (color, keypoints);
for (auto kp : keypoints) {
// 去掉邻近边缘处的点
if (kp.pt.x<20 || kp.pt.y<20 || (kp.pt.x+20)>color.cols || (kp.pt.y+20)>color.rows) continue;
ushort d = depth.ptr<ushort> (cvRound(kp.pt.y)) [cvRound (kp.pt.x)];
if (d == 0) continue;
Eigen::Vector3d p3d = project2Dto3D ( kp.pt.x, kp.pt.y, d, fx, fy, cx, cy, depth_scale );
float grayscale = float ( gray.ptr<uchar> ( cvRound ( kp.pt.y ) ) [ cvRound ( kp.pt.x ) ] );
measurements.push_back(Measurement(p3d, grayscale));
}
prev_color = color.clone();
continue;
}
// 使用直接法计算相机运动
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
poseEstimationDirect (measurements, &gray, K, Tcw); //直接法
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>> (t2-t1);
cout<<"direct method costs time: "<<time_used.count() <<"seconds."<<endl;
cout << "Tcw=" << Tcw.matrix() << endl;
// plot the feature points
cv::Mat img_show (color.rows*2, color.cols, CV_8UC3);
prev_color.copyTo(img_show(cv::Rect (0, 0, color.cols, color.rows)));
color.copyTo(img_show(cv::Rect(0, color.rows, color.cols, color.rows)));
for (Measurement m : measurements) {
if ( rand() > RAND_MAX/5 )
continue;
Eigen::Vector3d p = m.pos_world;
Eigen::Vector2d pixel_prev = project3Dto2D (p(0,0), p(1,0), p(2,0), fx, fy, cx, cy);
Eigen::Vector3d p2 = Tcw * m.pos_world;
Eigen::Vector2d pixel_now = project3Dto2D (p2(0,0), p2(1,0), p2(2,0), fx, fy, cx, cy);
if (pixel_now(0,0)<0 || pixel_now(0,0)>=color.cols ||
pixel_now(1,0)<0 || pixel_now(1,0)>=color.rows) continue;
float b = 255 * float(rand()) / RAND_MAX;
float g = 255 * float(rand()) / RAND_MAX;
float r = 255 * float(rand()) / RAND_MAX;
cv::circle (img_show, cv::Point2d(pixel_prev(0,0), pixel_prev(1,0)), 8, cv::Scalar(b,g,r), 2);
cv::circle(img_show, cv::Point2d(pixel_now(0,0), pixel_now(1,0) +color.rows), 8, cv::Scalar(b,g,r), 2);
cv::line (img_show, cv::Point2d(pixel_prev(0,0), pixel_prev(1,0)), cv::Point2d(pixel_now(0,0), pixel_now(1,0)+color.rows), cv::Scalar (b,g,r), 1);
}
cv::imshow ( "result", img_show );
cv::waitKey ( 0 );
}
return 0;
}
//直接法实现位姿估计
bool poseEstimationDirect ( const vector< Measurement >& measurements, cv::Mat* gray, Eigen::Matrix3f& K, Eigen::Isometry3d& Tcw ) {
// 初始化g2o
typedef g2o::BlockSolver<g2o::BlockSolverTraits<6,1>> DirectBlock; // 求解的向量是6＊1的
DirectBlock::LinearSolverType* linearSolver = new g2o::LinearSolverDense< DirectBlock::PoseMatrixType > ();
DirectBlock* solver_ptr = new DirectBlock ( linearSolver );
// g2o::OptimizationAlgorithmGaussNewton* solver = new g2o::OptimizationAlgorithmGaussNewton( solver_ptr ); // G-N 高斯牛顿法
g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( solver_ptr ); // L-M
g2o::SparseOptimizer optimizer;
optimizer.setAlgorithm ( solver );
optimizer.setVerbose( true );
g2o::VertexSE3Expmap* pose = new g2o::VertexSE3Expmap();
pose->setEstimate ( g2o::SE3Quat ( Tcw.rotation(), Tcw.translation() ) );
pose->setId ( 0 );
optimizer.addVertex ( pose );
// 添加边
int id = 1;
for (Measurement m : measurements) {
EdgeSE3ProjectDirect* edge = new EdgeSE3ProjectDirect (
m.pos_world, K(0,0), K(1,1), K(0,2), K(1,2), gray);
edge->setVertex ( 0, pose );
edge->setMeasurement ( m.grayscale );
edge->setInformation ( Eigen::Matrix<double,1,1>::Identity() );
edge->setId ( id++ );
optimizer.addEdge ( edge );
}
cout << "edges in graph: " << optimizer.edges().size() << endl;
optimizer.initializeOptimization();
optimizer.optimize ( 30 );
Tcw = pose->estimate();
}
（2）编译
mkdir build
cd build
cmake ..
make
cd ..
./build/direct_sparse ../data
8.5.4 半稠密直接法
（1）代码详解
CMakeLists.txt
cmake_minimum_required( VERSION 2.8 )
project( directMethod )
set( CMAKE_BUILD_TYPE Release )
set( CMAKE_CXX_FLAGS "-std=c++11 -O3" )
# 添加cmake模块路径
list( APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake_modules )
find_package( OpenCV )
include_directories( ${OpenCV_INCLUDE_DIRS} )
find_package( G2O )
include_directories( ${G2O_INCLUDE_DIRS} )
include_directories( "/usr/include/eigen3" )
set( G2O_LIBS
g2o_core g2o_types_sba g2o_solver_csparse g2o_stuff g2o_csparse_extension
)
#add_executable( direct_sparse direct_sparse.cpp )
#target_link_libraries( direct_sparse ${OpenCV_LIBS} ${G2O_LIBS} )
add_executable( direct_semidense direct_semidense.cpp )
target_link_libraries( direct_semidense ${OpenCV_LIBS} ${G2O_LIBS} )
directMethod.cpp
#include <iostream>
#include <fstream>
#include <list>
#include <vector>
#include <chrono>
#include <ctime>
#include <climits>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <g2o/core/base_unary_edge.h>
#include <g2o/core/block_solver.h>
#include <g2o/core/optimization_algorithm_levenberg.h>
#include <g2o/solvers/dense/linear_solver_dense.h>
#include <g2o/core/robust_kernel.h>
#include <g2o/types/sba/types_six_dof_expmap.h>
using namespace std;
using namespace g2o;
/********************************************
* 本节演示了RGBD上的半稠密直接法
********************************************/
// 一次测量的值，包括一个世界坐标系下三维点与一个灰度值
struct Measurement
{
Measurement(Eigen::Vector3d p, float g) : pos_world (p), grayscale (g) {}
Eigen::Vector3d pos_world;
float grayscale;
};
inline Eigen::Vector3d project2Dto3D ( int x, int y, int d, float fx, float fy, float cx, float cy, float scale ) {
float zz = float ( d ) /scale;
float xx = zz* ( x-cx ) /fx;
float yy = zz* ( y-cy ) /fy;
return Eigen::Vector3d ( xx, yy, zz );
}
inline Eigen::Vector2d project3Dto2D ( float x, float y, float z, float fx, float fy, float cx, float cy ) {
float u = fx*x/z+cx;
float v = fy*y/z+cy;
return Eigen::Vector2d ( u,v );
}
// 直接法估计位姿
// 输入：测量值（空间点的灰度），新的灰度图，相机内参； 输出：相机位姿
// 返回：true为成功，false失败
bool poseEstimationDirect ( const vector<Measurement>& measurements, cv::Mat* gray, Eigen::Matrix3f& intrinsics, Eigen::Isometry3d& Tcw );
// project a 3d point into an image plane, the error is photometric error
// an unary edge with one vertex SE3Expmap (the pose of camera)
class EdgeSE3ProjectDirect: public BaseUnaryEdge< 1, double, VertexSE3Expmap>
{
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
EdgeSE3ProjectDirect() {}
EdgeSE3ProjectDirect ( Eigen::Vector3d point, float fx, float fy, float cx, float cy, cv::Mat* image )
: x_world_ ( point ), fx_ ( fx ), fy_ ( fy ), cx_ ( cx ), cy_ ( cy ), image_ ( image )
{}
virtual void computeError()
{
const VertexSE3Expmap* v =static_cast<const VertexSE3Expmap*> ( _vertices[0] );
Eigen::Vector3d x_local = v->estimate().map ( x_world_ );
float x = x_local[0]*fx_/x_local[2] + cx_;
float y = x_local[1]*fy_/x_local[2] + cy_;
// check x,y is in the image
if ( x-4<0 || ( x+4 ) >image_->cols || ( y-4 ) <0 || ( y+4 ) >image_->rows )
{
_error ( 0,0 ) = 0.0;
this->setLevel ( 1 );
}
else
{
_error ( 0,0 ) = getPixelValue ( x,y ) - _measurement;
}
}
// plus in manifold
virtual void linearizeOplus( )
{
if ( level() == 1 )
{
_jacobianOplusXi = Eigen::Matrix<double, 1, 6>::Zero();
return;
}
VertexSE3Expmap* vtx = static_cast<VertexSE3Expmap*> ( _vertices[0] );
Eigen::Vector3d xyz_trans = vtx->estimate().map ( x_world_ ); // q in book
double x = xyz_trans[0];
double y = xyz_trans[1];
double invz = 1.0/xyz_trans[2];
double invz_2 = invz*invz;
float u = x*fx_*invz + cx_;
float v = y*fy_*invz + cy_;
// jacobian from se3 to u,v
// NOTE that in g2o the Lie algebra is (\omega, \epsilon), where \omega is so(3) and \epsilon the translation
Eigen::Matrix<double, 2, 6> jacobian_uv_ksai;
jacobian_uv_ksai ( 0,0 ) = - x*y*invz_2 *fx_;
jacobian_uv_ksai ( 0,1 ) = ( 1+ ( x*x*invz_2 ) ) *fx_;
jacobian_uv_ksai ( 0,2 ) = - y*invz *fx_;
jacobian_uv_ksai ( 0,3 ) = invz *fx_;
jacobian_uv_ksai ( 0,4 ) = 0;
jacobian_uv_ksai ( 0,5 ) = -x*invz_2 *fx_;
jacobian_uv_ksai ( 1,0 ) = - ( 1+y*y*invz_2 ) *fy_;
jacobian_uv_ksai ( 1,1 ) = x*y*invz_2 *fy_;
jacobian_uv_ksai ( 1,2 ) = x*invz *fy_;
jacobian_uv_ksai ( 1,3 ) = 0;
jacobian_uv_ksai ( 1,4 ) = invz *fy_;
jacobian_uv_ksai ( 1,5 ) = -y*invz_2 *fy_;
Eigen::Matrix<double, 1, 2> jacobian_pixel_uv;
jacobian_pixel_uv ( 0,0 ) = ( getPixelValue ( u+1,v )-getPixelValue ( u-1,v ) ) /2;
jacobian_pixel_uv ( 0,1 ) = ( getPixelValue ( u,v+1 )-getPixelValue ( u,v-1 ) ) /2;
_jacobianOplusXi = jacobian_pixel_uv*jacobian_uv_ksai;
}
// dummy read and write functions because we don't care...
virtual bool read ( std::istream& in ) {}
virtual bool write ( std::ostream& out ) const {}
protected:
// get a gray scale value from reference image (bilinear interpolated)
inline float getPixelValue ( float x, float y )
{
uchar* data = & image_->data[ int ( y ) * image_->step + int ( x ) ];
float xx = x - floor ( x );
float yy = y - floor ( y );
return float (
( 1-xx ) * ( 1-yy ) * data[0] +
xx* ( 1-yy ) * data[1] +
( 1-xx ) *yy*data[ image_->step ] +
xx*yy*data[image_->step+1]
);
}
public:
Eigen::Vector3d x_world_; // 3D point in world frame
float cx_=0, cy_=0, fx_=0, fy_=0; // Camera intrinsics
cv::Mat* image_=nullptr; // reference image
};
int main ( int argc, char** argv )
{
if ( argc != 2 ) {
cout << "usage: useLK path_to_dataset" << endl;
return 1;
}
srand ( ( unsigned int ) time ( 0 ) );
string path_to_dataset = argv[1];
string associate_file = path_to_dataset + "/associate.txt";
ifstream fin ( associate_file );
string rgb_file, depth_file, time_rgb, time_depth;
cv::Mat color, depth, gray;
vector<Measurement> measurements;
// 相机内参
float cx = 325.5;
float cy = 253.5;
float fx = 518.0;
float fy = 519.0;
float depth_scale = 1000.0;
Eigen::Matrix3f K;
K<<fx,0.f,cx,0.f,fy,cy,0.f,0.f,1.0f;
Eigen::Isometry3d Tcw = Eigen::Isometry3d::Identity();
cv::Mat prev_color;
// 我们以第一个图像为参考，对后续图像和参考图像做直接法
for ( int index=0; index<10; index++ ) {
cout << "*********** loop " << index << " ************" << endl;
fin >> time_rgb >> rgb_file >> time_depth >> depth_file;
color = cv::imread(path_to_dataset + "/" + rgb_file);
depth = cv::imread(path_to_dataset + "/" + depth_file, -1);
if ( color.data==nullptr || depth.data==nullptr )
continue;
cv::cvtColor ( color, gray, cv::COLOR_BGR2GRAY );
if ( index ==0 ) {
// select the pixels with high gradiants
for ( int x=10; x<gray.cols-10; x++ )
for ( int y=10; y<gray.rows-10; y++ ) {
Eigen::Vector2d delta (
gray.ptr<uchar>(y)[x+1] - gray.ptr<uchar>(y)[x-1],
gray.ptr<uchar>(y+1)[x] - gray.ptr<uchar>(y-1)[x]
);
if ( delta.norm() < 50 )
continue;
ushort d = depth.ptr<ushort> (y)[x];
if ( d==0 )
continue;
Eigen::Vector3d p3d = project2Dto3D ( x, y, d, fx, fy, cx, cy, depth_scale );
float grayscale = float ( gray.ptr<uchar> (y) [x] );
measurements.push_back ( Measurement ( p3d, grayscale ) );
}
prev_color = color.clone();
cout<<"add total "<<measurements.size()<<" measurements."<<endl;
continue;
}
// 使用直接法计算相机运动
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
poseEstimationDirect ( measurements, &gray, K, Tcw );
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>> ( t2-t1 );
cout<<"direct method costs time: "<<time_used.count()<<" seconds."<<endl;
cout << "Tcw=" << Tcw.matrix() << endl;
// plot the feature points
cv::Mat img_show ( color.rows*2, color.cols, CV_8UC3 );
prev_color.copyTo(img_show(cv::Rect(0,0,color.cols, color.rows)));
color.copyTo(img_show(cv::Rect(0,color.rows,color.cols, color.rows)));
for (Measurement m : measurements) {
if ( rand() > RAND_MAX/5 )
continue;
Eigen::Vector3d p = m.pos_world;
Eigen::Vector2d pixel_prev = project3Dto2D ( p ( 0,0 ), p ( 1,0 ), p ( 2,0 ), fx, fy, cx, cy );
Eigen::Vector3d p2 = Tcw*m.pos_world;
Eigen::Vector2d pixel_now = project3Dto2D ( p2 ( 0,0 ), p2 ( 1,0 ), p2 ( 2,0 ), fx, fy, cx, cy );
if ( pixel_now(0,0)<0 || pixel_now(0,0)>=color.cols || pixel_now(1,0)<0 || pixel_now(1,0)>=color.rows )
continue;
float b = 0;
float g = 250;
float r = 0;
img_show.ptr<uchar>( pixel_prev(1,0) )[int(pixel_prev(0,0))*3] = b;
img_show.ptr<uchar>( pixel_prev(1,0) )[int(pixel_prev(0,0))*3+1] = g;
img_show.ptr<uchar>( pixel_prev(1,0) )[int(pixel_prev(0,0))*3+2] = r;
img_show.ptr<uchar>( pixel_now(1,0)+color.rows )[int(pixel_now(0,0))*3] = b;
img_show.ptr<uchar>( pixel_now(1,0)+color.rows )[int(pixel_now(0,0))*3+1] = g;
img_show.ptr<uchar>( pixel_now(1,0)+color.rows )[int(pixel_now(0,0))*3+2] = r;
cv::circle ( img_show, cv::Point2d ( pixel_prev ( 0,0 ), pixel_prev ( 1,0 ) ), 4, cv::Scalar ( b,g,r ), 2 );
cv::circle ( img_show, cv::Point2d ( pixel_now ( 0,0 ), pixel_now ( 1,0 ) +color.rows ), 4, cv::Scalar ( b,g,r ), 2 );
}
cv::imshow ( "result", img_show );
cv::waitKey ( 0 );
}
return 0;
}
bool poseEstimationDirect ( const vector< Measurement >& measurements, cv::Mat* gray, Eigen::Matrix3f& K, Eigen::Isometry3d& Tcw )
{
// 初始化g2o
typedef g2o::BlockSolver<g2o::BlockSolverTraits<6,1>> DirectBlock; // 求解的向量是6＊1的
DirectBlock::LinearSolverType* linearSolver = new g2o::LinearSolverDense< DirectBlock::PoseMatrixType > ();
DirectBlock* solver_ptr = new DirectBlock ( linearSolver );
// g2o::OptimizationAlgorithmGaussNewton* solver = new g2o::OptimizationAlgorithmGaussNewton( solver_ptr ); // G-N
g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( solver_ptr ); // L-M
g2o::SparseOptimizer optimizer;
optimizer.setAlgorithm ( solver );
optimizer.setVerbose( true );
g2o::VertexSE3Expmap* pose = new g2o::VertexSE3Expmap();
pose->setEstimate ( g2o::SE3Quat ( Tcw.rotation(), Tcw.translation() ) );
pose->setId ( 0 );
optimizer.addVertex ( pose );
// 添加边
int id=1;
for ( Measurement m : measurements )
{
EdgeSE3ProjectDirect* edge = new EdgeSE3ProjectDirect (
m.pos_world,
K ( 0,0 ), K ( 1,1 ), K ( 0,2 ), K ( 1,2 ), gray
);
edge->setVertex ( 0, pose );
edge->setMeasurement ( m.grayscale );
edge->setInformation ( Eigen::Matrix<double,1,1>::Identity() );
edge->setId ( id++ );
optimizer.addEdge ( edge );
}
cout<<"edges in graph: "<<optimizer.edges().size() <<endl;
optimizer.initializeOptimization();
optimizer.optimize ( 30 );
Tcw = pose->estimate();
}
（2）编译
mkdir build
cd build
cmake ..
make
cd ..
./build/direct_semidense ../data
