平台：Ubuntu 16.04
相机： MER-301-125U3M (大恒其他型号的USB相机亦可)
本文是在原有ORB-SLAM源码基础上增加大恒相机的运行示例，用大恒相机实时运行ORB-SLAM。
主要涉及两个地方得修改：
- 采图。之前的example是读本地图片传入
ORB_SLAM2::System，现在改成用大恒相机实时采图传入 - 相机参数。之前的example是用数据集已有的参数，现在是换成自己相机的实际参数
1. 安装大恒的SDK
从大恒官网下载linux的安装包，名称如Galaxy_Linux-x86_Gige-U3_32bits-64bits_1.2.1911.9122.tar.gz
解压后进入Galaxy_Linux-x86_Gige-U3_32bits-64bits_1.2.1911.9122目录，
在终端中运行./Galaxy_camera.run，便会生成安装目录Galaxy_camera
大恒的头文件和库文件:
头文件： 在./Galaxy_camera/inc目录，有DxImageProc.h和GxIAPI.h两个文件
库文件： 在系统库目录下，/usr/lib/libgxiapi.so，所以cmake的时候可以直接去链接
2. 下载 ORB-SLAM2 源码
github官方库： https://github.com/raulmur/ORB_SLAM2
下载源码：
git clone https://github.com/raulmur/ORB_SLAM2.git ORB_SLAM2
以下所有操作的根目录为上述下载的路径ORB_SLAM2。
首先按照上面的库的github首页的编译操作，安装好依赖后，编译源码。
保证源码自身编译没有问题之后，再进行以下步骤。
3. 用大恒相机运行ORB-SLAM
是在原有的Example基础上，加一个用大恒相机运行ORB-SLAM的例子
3.1 涉及修改和新增的目录结构如下
.
├── CMakeLists.txt (修改)
├── DxImageProc.h (新增)
├── GxIAPI.h (新增)
└── Examples
└── Monocular
├── mono_daheng.cc (新增)
└── daheng.yaml (新增)
-
.为根目录ORB_SLAM2 -
GxIAPI.h和DxImageProc.h为大恒的头文件，从大恒安装目录的inc目录里复制过来 -
mono_daheng.cc为新增的用大恒相机运行ORB-SLAM的源文件 -
daheng.yaml为新增的大恒相机的参数文件
3.2 修改根目录下的CMakeLists.txt
在原有的CMakeLists.txt的文件末尾加上如下内容
add_executable(mono_daheng
Examples/Monocular/mono_daheng.cc)
target_link_libraries(mono_daheng ${PROJECT_NAME} gxiapi)
3.3 daheng.yaml
在 ./Examples/Monocular/ 目录下 创建 daheng.yaml 文件，是大恒相机的参数文件，
录入内容如下，里面的相机参数可根据自己相机的情况录入。
关于相机的标定，可以自己写opencv标定程序来标定。
或者使用现有的标定程序来标定，比如ros里有个camera_calibration程序可用于标定。
%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 2360.89
Camera.fy: 2363.79
Camera.cx: 1029.18
Camera.cy: 821.37
Camera.k1: -0.095
Camera.k2: 0.353
Camera.p1: 0.0
Camera.p2: 0.0
# Camera frames per second
Camera.fps: 20.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500
3.4 mono_daheng.cc
在 ./Examples/Monocular/ 目录下 创建 mono_daheng.cc 文件，是用大恒相机运行ORB-SLAM的源文件，
录入内容如下：
#include
#include
#include
#include
#include
#include
#include
#include "GxIAPI.h"
#include "DxImageProc.h"
using namespace std;
//Show error message
#define GX_VERIFY(emStatus) \
if (emStatus != GX_STATUS_SUCCESS) \
{ \
GetErrorString(emStatus); \
return emStatus; \
}
//Show error message, close device and lib
#define GX_VERIFY_EXIT(emStatus) \
if (emStatus != GX_STATUS_SUCCESS) \
{ \
GetErrorString(emStatus); \
GXCloseDevice(hDevice); \
hDevice = NULL; \
GXCloseLib(); \
printf("\n"); \
return emStatus; \
}
//Get description of error
void GetErrorString(GX_STATUS);
int main(int argc, char **argv)
{
if(argc != 3)
{
cerr << endl << "Usage: ./mono_tum path_to_vocabulary path_to_settings" << endl;
return 1;
}
int nImages = 1500; // use 1500 images
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::MONOCULAR,true);
// Vector for tracking time statistics
vector vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl << "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "The number of images to be used: " << nImages << endl << endl;
// daheng
GX_STATUS status = GX_STATUS_SUCCESS;
GX_DEV_HANDLE hDevice = NULL;
uint32_t nDeviceNum = 0;
//Initialize libary
status = GXInitLib();
if (status != GX_STATUS_SUCCESS) {
GetErrorString(status);
GXCloseLib();
return 0;
}
//Get device enumerated number
status = GXUpdateDeviceList(&nDeviceNum, 1000);
if (status != GX_STATUS_SUCCESS) {
GetErrorString(status);
GXCloseLib();
return 0;
}
//If no device found, app exit
if(nDeviceNum <= 0) {
printf("\n");
GXCloseLib();
return 0;
}
//Open first device enumerated
status = GXOpenDeviceByIndex(1, &hDevice);
if (status != GX_STATUS_SUCCESS) {
GetErrorString(status);
GXCloseLib();
return 0;
}
//Set acquisition mode
status = GXSetEnum(hDevice, GX_ENUM_ACQUISITION_MODE, GX_ACQ_MODE_CONTINUOUS);
GX_VERIFY_EXIT(status);
//Set trigger mode
status = GXSetEnum(hDevice, GX_ENUM_TRIGGER_MODE, GX_TRIGGER_MODE_OFF);
GX_VERIFY_EXIT(status);
//Device start acquisition
status = GXStreamOn(hDevice);
if(status != GX_STATUS_SUCCESS) {
GX_VERIFY_EXIT(status);
}
PGX_FRAME_BUFFER pFrameBuffer = NULL;
// Main loop
cv::Mat im;
struct timeval tv;
for(int ni=0; ninStatus != GX_FRAME_STATUS_SUCCESS) {
printf("\n", pFrameBuffer->nStatus);
}
// image
int width = pFrameBuffer->nWidth;
int height = pFrameBuffer->nHeight;
// cout <<"pFrameBuffer size: "<< width <<"x"<pImgBuf, cv::Mat::AUTO_STEP);
im = image.clone();
gettimeofday(&tv, NULL);
double tframe = (double)(tv.tv_sec) + (tv.tv_usec*1.0)/1000000.0;
if(im.empty())
{
cerr << endl << "im is empty: " << endl;
continue;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the image to the SLAM system
SLAM.TrackMonocular(im,tframe);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack= std::chrono::duration_cast >(t2 - t1).count();
vTimesTrack[ni]=ttrack;
// // Wait to load the next frame
// double T=0;
// if(ni0)
// T = tframe-vTimestamps[ni-1];
// if(ttrack\n");
return;
}
// Alloc error resources
error_info = new char[size];
if (error_info == NULL)
{
printf("\n");
return ;
}
// Get error description
emStatus = GXGetLastError(&emErrorStatus, error_info, &size);
if (emStatus != GX_STATUS_SUCCESS)
{
printf("\n");
}
else
{
printf("%s\n", (char*)error_info);
}
// Realease error resources
if (error_info != NULL)
{
delete []error_info;
error_info = NULL;
}
}
3.5 编译运行
在根目录下执行如下脚本，重新编译修改后的源码
./build.sh
编译成功后会在./Examples/Monocular/目录下生成可执行程序mono_daheng，这个便是用大恒相机运行ORB-SLAM的可执行程序，
按照如下方式传参运行：
./Examples/Monocular/mono_daheng Vocabulary/ORBvoc.txt Examples/Monocular/daheng.yaml
运行时保证大恒相机已经连接上，否则不能采图。
4. 参考
关于大恒相机在linux的使用，可以参考之前的一篇博客：
linux下用大恒相机采图
https://www.jianshu.com/p/eb490fae2cf7
关于相机的标定，从网上找到一篇使用ros的camera_calibration包的博客，但是没有尝试过，大家可以尝试一下：
Ubuntu 16.04 ROS标定相机——无坑篇
https://blog.csdn.net/qq_36804363/article/details/89269776