Segmentation fault (core dumped) 和double free or corruption (out)Aborted (core dumped)
代码链接：GitHub - gaoxiang12/slambookContribute to gaoxiang12/slambook development by creating an account on GitHub.
https://github.com/gaoxiang12/slambook今天在编译高博视觉SLAM十四讲第一版的第八章代码时候出现了两个错误，虽然解决了，但是不是道为什么会这样，本来是买的第二版的书，但是在B站上看到的视频和第二版不一样，于是找出来编译看看，里面有代码需要修改，修改后的代码如下：
direct_semidense.cpp：
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
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& 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 ( _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::Zero();
return;
}
VertexSE3Expmap* vtx = static_cast ( _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 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 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"< 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<>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(y)[x+1] - gray.ptr(y)[x-1],
gray.ptr(y+1)[x] - gray.ptr(y-1)[x]
);
if ( delta.norm() < 50 )
continue;
ushort d = depth.ptr (y)[x];
if ( d==0 )
continue;
Eigen::Vector3d p3d = project2Dto3D ( x, y, d, fx, fy, cx, cy, depth_scale );
float grayscale = float ( gray.ptr (y) [x] );
measurements.push_back ( Measurement ( p3d, grayscale ) );
}
prev_color = color.clone();
cout<<"add total "< time_used = chrono::duration_cast> ( t2-t1 );
cout<<"direct method costs time: "<& measurements, cv::Mat* gray, Eigen::Matrix3f& K, Eigen::Isometry3d& Tcw )
{
// 初始化g2o
typedef g2o::BlockSolver> 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
std::unique_ptr linearSolver ( new g2o::LinearSolverDense());
std::unique_ptr solver_ptr ( new DirectBlock ( std::move(linearSolver)));
g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( std::move(solver_ptr));
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::Identity() );
edge->setId ( id++ );
optimizer.addEdge ( edge );
}
cout<<"edges in graph: "<estimate();
}
direct_sparse.cpp：
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
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& 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 ( _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::Zero();
return;
}
VertexSE3Expmap* vtx = static_cast ( _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 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 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"< 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<>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(y)[x+1] - gray.ptr(y)[x-1],
gray.ptr(y+1)[x] - gray.ptr(y-1)[x]
);
if ( delta.norm() < 50 )
continue;
ushort d = depth.ptr (y)[x];
if ( d==0 )
continue;
Eigen::Vector3d p3d = project2Dto3D ( x, y, d, fx, fy, cx, cy, depth_scale );
float grayscale = float ( gray.ptr (y) [x] );
measurements.push_back ( Measurement ( p3d, grayscale ) );
}
prev_color = color.clone();
cout<<"add total "< time_used = chrono::duration_cast> ( t2-t1 );
cout<<"direct method costs time: "<& measurements, cv::Mat* gray, Eigen::Matrix3f& K, Eigen::Isometry3d& Tcw )
{
// 初始化g2o
typedef g2o::BlockSolver> 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
std::unique_ptr linearSolver ( new g2o::LinearSolverDense());
std::unique_ptr solver_ptr ( new DirectBlock ( std::move(linearSolver)));
g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( std::move(solver_ptr));
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::Identity() );
edge->setId ( id++ );
optimizer.addEdge ( edge );
}
cout<<"edges in graph: "<estimate();
}
CMakeLists.txt：
cmake_minimum_required( VERSION 2.8 )
project( directMethod )
#set( CMAKE_BUILD_TYPE Release )
#set( CMAKE_CXX_FLAGS "-std=c++14 -O3" )
set(CMAKE_BUILD_TYPE "Release")
add_definitions("-DENABLE_SSE")
set(CMAKE_CXX_FLAGS "-std=c++14 ${SSE_FLAGS} -g -O3 -march=native")
#set( CMAKE_CXX_FLAGS "-std=c++14 -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(
${OpenCV_INCLUDE_DIRS}
${G2O_INCLUDE_DIRS}
${Sophus_INCLUDE_DIRS}
"/usr/include/eigen3/"
${Pangolin_INCLUDE_DIRS}
)
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} )
double free or corruption (out)Aborted (core dumped)
解决办法：将set(CMAKE_CXX_FLAGS "-std=c++14 ${SSE_FLAGS} -g -O3 -march=native")
改为set( CMAKE_CXX_FLAGS "-std=c++14 -O3" )就不会报错了，也不知道为什么会这样。
然后在这台电脑(AMD)电脑上执行时，
又出现了 Segmentation fault (core dumped)这个错误
然后我换了个电脑，是i7的电脑，执行时问题就解决了，也不知道为什么会这样，有可能是平台的原因。