ORB-SLAM3运行自制数据集进行定位教程

目前手上有一个特定的任务，做应急救援的视觉SLAM，目前公共数据集比较少，考虑自建数据集，从网络上爬虫火灾、地震的等手机录制的视频，应用一些现有成熟ORB-SLAM3系统到这个数据集上看效果，然后根据效果得到一些模型改进思路。首先制作相机标定文件，然后使用Cmake工程结合ORB-SLAM3，接着爬虫获取智能手机录制应急救援场景的视频文件，对应时间戳信息，将视频分成多帧图片，最后得到自制数据集。

一、系统配置

系统	版本
ubuntu	20.04
OpenCV	3.4.13
Eigen	3.2.10
Pangolin	0.6

二、制作数据集

1、脚本编写

#include <opencv2/opencv.hpp>
#include "System.h"
#include <string>
#include <chrono>   // for time stamp
#include <iostream>
using namespace std;
// 参数文件与字典文件
// 如果你系统上的路径不同，请修改它
string parameterFile = "./test.yaml";
string vocFile = "./Vocabulary/ORBvoc.txt";
// 视频文件，若不同请修改
string videoFile = "./test.mp4";
int main(int argc, char **argv) {
 // 声明 ORB-SLAM4 系统
    ORB_SLAM2::System SLAM(vocFile, parameterFile, ORB_SLAM2::System::MONOCULAR, true);
 // 获取视频图像
  cv::VideoCapture cap(videoFile);    // 参数0为usb相机
  // 记录系统时间
  auto start = chrono::system_clock::now();

while (1) {
        cv::Mat frame;
        cap >> frame;   // 读取相机数据

        if ( frame.data == nullptr )
            continue;
        // rescale because image is too large
        cv::Mat frame_resized;
        cv::resize(frame, frame_resized, cv::Size(640,360));
        auto now = chrono::system_clock::now();
        auto timestamp = chrono::duration_cast<chrono::milliseconds>(now - start);
        SLAM.TrackMonocular(frame_resized, double(timestamp.count())/1000.0);
        cv::waitKey(30);
    }
    return 0;


}

2、配置文件编写

%YAML:1.0
 
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
 
# Camera calibration and distortion parameters (OpenCV) 
Camera.fx: 500.0
Camera.fy: 500.0
Camera.cx: 320.0
Camera.cy: 240.0
 
Camera.k1: 0.262383
Camera.k2: -0.953104
Camera.p1: -0.005358
Camera.p2: 0.002628
Camera.k3: 1.163314
 
# Camera frames per second 
Camera.fps: 30.0
 
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 0
 
#--------------------------------------------------------------------------------------------
# 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: 10
ORBextractor.minThFAST: 5
 
#--------------------------------------------------------------------------------------------
# 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

将两个文件复制到ORB_SLAM3根目录下

3、录制视频素材

（1）将手机横向拍摄
（2）开始拍摄时，首先手机左右缓慢水平移动，为了是ORB-SLAM3初始化正常，像“螃蟹”一样左右移动小步即可
（3）五秒左右，再慢慢往前走，不要走的太快，转弯时不要太快，防止跟踪丢失
（4）录制完成后，将其复制到ORB_SLAM3文件下，重命名为test.mp4

4、修改CMakeLists.txt

修改ORB_SLAM3里面的CMakeLists.txt，添加如下代码：

set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR})
add_executable(test test.cpp)
target_link_libraries(test ${PROJECT_NAME})

5、编译运行

cd ORB_SLAM3
mkdir build
cd build
cmake ..
make -j

cd ..
./test