双目相机标定和orbslam2双目参数详解

Keywords
orbslam2 orbslam orbslam参数 orbslam双目 orbslam2双目 orbslam双目摄像头 orbslam双目参数详解 orbslam2双目参数详解 orb_slam2双目参数标定 orbslam2双目参数标定 orb_slam双目参数详解 orb_slam2双目参数详解 orb_slam2双目相机参数 orb_slam2双目相机参数 标定

为什么要写这篇博客?
因为网上的博客不适合当下的ORB_SLAM2双目标定,特别是针对广角相机标定的暂无发现可用教程。而对orbslam2参数讲解的文档,全部来自于高博的博客,对于太多的人来说很多东西含糊其次,阐述的不知所云。为了帮助后来者于2019年写下此博客以供参考,以杜绝一些废文耽误后来者之现象。
本博客有什么内容?
1,orb_slam2 双目摄像机的标定程序
2,orb_slam2 双目配置参数详解。
3,orb_slam2 摄像头配套的调用代码在下一篇博客给出。
一,对标orb_slam2 的双目摄像机标定程序(广角、平角都可)
经实测,opencv sample里面自带的双目标定程序(stereo_clib.cpp),对平角相机效果较好,如果你是平角相机跳过本段,直接查看ORBSLAM2的双目配置参数,而stereo_clib.cpp对广角相机效果奇差。
故十里桃园综合了很多帖子,再根据经验给出下述双目代码(基于opencv342测试 opencv3 vs各版本下载地址: 链接: https://pan.baidu.com/s/1f5oAFqs-u15vkD5LNTcxtw 提取码: 2qj9),另用vs19跑程序时有无相应现象,应为vs19bug,在linux下实测可行:
这里要强调的是,双目切勿左右搞反,可对照opencv给出的左右图像来确定自己是否搞反。
再说明一点:请保证图像序号的连续,还要保证图像序列中没有找不到角点的图像。程序会有提示没有成功找到角点的图像序号,删除,重新对图像编号。保证图像序号的连续。图像序号从 1 开始 ,代码中的frameNumber 设置为 你图像序号最大值 +1,如十里桃园一共用58图像序列,则设为59.

先自行标定左右相机的单目畸变参数,填入下面代码中的初始化参数,楼主用的kalibr标定的单目,opencv也可以标定,这个自己解决。
说明一点,单目预标定没必要标的很精确,差不多就成,当然标的精确更好,畸变参数4,5个都可以,单目的所有预标定参数将在下面的代码中进行迭代优化。

修改你的棋盘信息 纵横角点数 还有每个格子的大小 单位mm
广角双目标定代码如下:

#include "opencv2/core/core.hpp" 
#include "opencv2/imgproc/imgproc.hpp"  
#include "opencv2/calib3d/calib3d.hpp"  
#include "opencv2/highgui/highgui.hpp"  
#include   
#include   
#include   
#include   
#include   
#include   
#include   
#include    
#include   
#include "cv.h"  
#include   
using namespace std;
using namespace cv;                                        
const int imageWidth = 640;                             
const int imageHeight = 480;
const int boardWidth = 11;                               
//横向的角点数目  
const int boardHeight = 8;                              
//纵向的角点数据  
const int boardCorner = boardWidth * boardHeight;      
//总的角点数据  
                            
//相机标定时需要采用的图像帧数  
const int squareSize = 49.27;                              
//标定板黑白格子的大小单位mm  

const int frameNumber = 59;
//图像命名 从1 ~ 58(59-1=58)
string folder_ = "./data/"; 
string format_R = "R";
string format_L = "L";
//例如: R1.jpg   L58.jpg 置于工程目录的 data文件夹下, 
const Size boardSize = Size(boardWidth, boardHeight);   
//标定板的总内角点  
Size imageSize = Size(imageWidth, imageHeight);Mat R, T, E, F;                                                 
//R 旋转矢量 T平移矢量 E本征矩阵 F基础矩阵  
vector rvecs;                                        
//旋转向量  
vector tvecs;                                        
//平移向量  
vector> 
imagePointL;                    
//左边摄像机所有照片角点的坐标集合  
vector> imagePointR;                    
//右边摄像机所有照片角点的坐标集合  
vector> objRealPoint;                  
//各副图像的角点的实际物理坐标集合  
vector cornerL;                              
//左边摄像机某一照片角点坐标集合  
vector cornerR;                              
//右边摄像机某一照片角点坐标集合  
Mat rgbImageL, grayImageL;
Mat rgbImageR, grayImageR;
Mat Rl, Rr, Pl, Pr, Q;                                 
//校正旋转矩阵R,投影矩阵P 重投影矩阵Q (下面有具体的含义解释)   
Mat mapLx, mapLy, mapRx, mapRy;                         
//映射表  
Rect validROIL, validROIR;                              
//图像校正之后,会对图像进行裁剪,这里的validROI就是指裁剪之后的区域 
/*事先标定好的左相机的内参矩阵
fx 0 cx
0 fy cy
0 0  1*/
Mat cameraMatrixL = (Mat_(3, 3) << 296.65731645541695, 0, 343.1975436071541,
										   0, 300.71016643747646, 246.01183552967473,
										   0, 0, 1);
//这时候就需要你把左右相机单目标定的参数给写上
//获得的畸变参数
Mat distCoeffL = (Mat_(4, 1) << -0.23906272129552558, 0.03436102573634348, 0.001517498429211239, -0.005280695866378259);
/*事先标定好的右相机的内参矩阵
fx 0 cx
0 fy cy
0 0  1*/
Mat cameraMatrixR = (Mat_(3, 3) << 296.92709649579353, 0, 313.1873142211607,
										   0, 300.0649937238372, 217.0722185756087,
										   0, 0, 1);
Mat distCoeffR = (Mat_(4, 1) << -0.23753878535018613, 0.03338842944635466, 0.0026030620085220105, -0.0008840126895030034);

void calRealPoint(vector>& obj, int boardwidth, int boardheight, int imgNumber, int squaresize)
	{ 	
		vector imgpoint;	
		for (int rowIndex = 0; rowIndex < boardheight; rowIndex++)	
			{		
				for (int colIndex = 0; colIndex < boardwidth; colIndex++)		
				{			
					imgpoint.push_back(Point3f(rowIndex * squaresize, colIndex * squaresize, 0));		
				}	
		}	
		for (int imgIndex = 0; imgIndex < imgNumber; imgIndex++)	
		{		
			obj.push_back(imgpoint);	
		}
}
void outputCameraParam(void)                   
{	/*保存数据*/	/*输出数据*/	
	FileStorage fs("intrinsics.yml", FileStorage::WRITE);  
	//文件存储器的初始化
	if (fs.isOpened())	
	{		
		fs << "cameraMatrixL" << cameraMatrixL << "cameraDistcoeffL" << distCoeffL << "cameraMatrixR" << cameraMatrixR << "cameraDistcoeffR" << distCoeffR;		
		fs.release();		
		cout << "cameraMatrixL=:" << cameraMatrixL << endl << "cameraDistcoeffL=:" << distCoeffL << endl << "cameraMatrixR=:" << cameraMatrixR << endl << "cameraDistcoeffR=:" << distCoeffR << endl;
	}	
	else	
	{		
		cout << "Error: can not save the intrinsics!!!!!" << endl;	
	}	
	fs.open("extrinsics.yml", FileStorage::WRITE);	
	if (fs.isOpened())	
	{		
		fs << "R" << R << "T" << T << "Rl" << Rl << "Rr" << Rr << "Pl" << Pl << "Pr" << Pr << "Q" << Q;
		cout << "R=" << R << endl << "T=" << T << endl << "Rl=" << Rl << endl << "Rr=" << Rr << endl << "Pl=" << Pl << endl << "Pr=" << Pr << endl << "Q=" << Q << endl;
		fs.release();	
	}	
	else		
		cout << "Error: can not save the extrinsic parameters\n";
}

int main(int argc, char* argv[])
{	
	Mat img;	
	int goodFrameCount = 1;
	cout << "Total Images:" << frameNumber << endl;
	while (goodFrameCount < frameNumber)	
	{	
		cout <<"Current image :" << goodFrameCount << endl;
		string 	filenamel,filenamer;
		//char filename[100];		
		/*读取左边的图像*/		
                filenamel = folder_ + format_L+	to_string(goodFrameCount)+".jpg";	
		rgbImageL = imread(filenamel, CV_LOAD_IMAGE_COLOR);		
		cvtColor(rgbImageL, grayImageL, CV_BGR2GRAY);
		/*读取右边的图像*/		
		//sprintf_s(filename, "D:/dual_camera_clibration/dual/R%d.jpg", goodFrameCount );	
                filenamer = folder_ + format_R+	to_string(goodFrameCount)+".jpg";	
		rgbImageR = imread(filenamer, CV_LOAD_IMAGE_COLOR);		
		cvtColor(rgbImageR, grayImageR, CV_BGR2GRAY);		
		bool isFindL, isFindR;		
		isFindL = findChessboardCorners(rgbImageL, boardSize, cornerL);		
		isFindR = findChessboardCorners(rgbImageR, boardSize, cornerR);		
		if (isFindL == true && isFindR == true)  
			//如果两幅图像都找到了所有的角点 则说明这两幅图像是可行的  		
		{						
			cornerSubPix(grayImageL, cornerL, Size(5, 5), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 20, 0.1));	
			drawChessboardCorners(rgbImageL, boardSize, cornerL, isFindL);
			imshow("chessboardL", rgbImageL);	
			imagePointL.push_back(cornerL);		
			cornerSubPix(grayImageR, cornerR, Size(5, 5), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 20, 0.1));	
			drawChessboardCorners(rgbImageR, boardSize, cornerR, isFindR);		
			imshow("chessboardR", rgbImageR);		
			imagePointR.push_back(cornerR);						
			goodFrameCount++;			
			cout << "The image" << goodFrameCount << " is good" << endl;	
		}		
		else		
		{			
			cout << "The image "<< goodFrameCount <<"is bad please try again" << endl;
			goodFrameCount++;
		}		

		if (waitKey(10) == 'q')		
		{			
			break;		
		}	
	}	
	/*	计算实际的校正点的三维坐标	根据实际标定格子的大小来设置	*/	
	calRealPoint(objRealPoint, boardWidth, boardHeight, frameNumber-1, squareSize);	
	cout << "cal real successful" << endl;
	/*	标定摄像头	由于左右摄像机分别都经过了单目标定	所以在此处选择flag = CALIB_USE_INTRINSIC_GUESS	*/	
	double rms = stereoCalibrate(objRealPoint, imagePointL, imagePointR,
		cameraMatrixL, distCoeffL,
		cameraMatrixR, distCoeffR,
		Size(imageWidth, imageHeight), R, T, E, F, CV_CALIB_USE_INTRINSIC_GUESS,
		TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 80, 1e-5));	
	cout << "Stereo Calibration done with RMS error = " << rms << endl;
	stereoRectify(cameraMatrixL, distCoeffL, cameraMatrixR, distCoeffR, imageSize, R, T, Rl, Rr, Pl, Pr, Q,		CALIB_ZERO_DISPARITY, -1, imageSize, &validROIL, &validROIR);   

	initUndistortRectifyMap(cameraMatrixL, distCoeffL, Rl, Pr, imageSize, CV_32FC1, mapLx, mapLy); 	
	initUndistortRectifyMap(cameraMatrixR, distCoeffR, Rr, Pr, imageSize, CV_32FC1, mapRx, mapRy);	
	Mat rectifyImageL, rectifyImageR;		
	cout << "debug"<

标定以前的图像其中一副图像(单纯展示一下我的相机畸变是啥样的):
双目相机标定和orbslam2双目参数详解_第1张图片
双目图像修正结果示例:
双目相机标定和orbslam2双目参数详解_第2张图片生成的外参extrinsics.yml、内参intrinsics.yml文件:
在这里插入图片描述

二,orb_slam2 双目配置参数详解
十里桃园参考orbslam2给的EuRoC数据集测试配置文件EuRoC.yaml进行修改。
首先,对EuRoC.yaml对标上述代码生成的extrinsics.yml、intrinsics.yml进行一项项详解,:

%YAML:1.0

#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------

# Camera calibration and distortion parameters (OpenCV) 
Camera.fx: 435.2046959714599
Camera.fy: 435.2046959714599
Camera.cx: 367.4517211914062
Camera.cy: 252.2008514404297
//
这个是 双目相机的参数不是单个的做相机的相机中心跟焦距。
其对应:extrinsics.yml中的 Pr:
例如我的是
Pr: !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [ 2.8559499458758660e+02, 0., 2.7029193305969238e+02,
       -3.9636548646706200e+04, 0., 2.8559499458758660e+02,
       2.8112063348293304e+02, 0., 0., 0., 1., 0. ]
对应的修改焦距和相机中心如下:
Camera.fx: 2.8559499458758660e+02
Camera.fy: 2.8559499458758660e+02
Camera.cx: 2.7029193305969238e+02
Camera.cy: 2.8112063348293304e+02
//

Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
//
默认不改,因代码中已做畸变纠正。故均为0.
//
Camera.width: 752
Camera.height: 480
//
相机的图像大小:
我的修改为:

Camera.width: 640
Camera.height: 480

//
# Camera frames per second 
Camera.fps: 20.0

# stereo baseline times fx
Camera.bf: 47.90639384423901
//
这个参数是个大坑,其为相机的基线×相机的焦距。
orbslam的参数文件中单位是m
而opencv标定文件中的单位是mm
其数值同样可以在Pr: 中找出 定位在下面矩阵中的-3.9636548646706200e+04 这个数
Pr: !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [ 2.8559499458758660e+02, 0., 2.7029193305969238e+02,
       -3.9636548646706200e+04, 0., 2.8559499458758660e+02,
       2.8112063348293304e+02, 0., 0., 0., 1., 0. ]

-3.9636548646706200e+04 就是要填入上面的参数,毫米转为米,求绝对值,填入Camera.bf:  3.9636548646706200e+01

//
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1

# Close/Far threshold. Baseline times.
ThDepth: 35
//
深度阈值,不是一个精确的数值,大概预估的,可以不改动,要改的话参考下述公式
自己粗略估计一个相机可以良好显示的最大距离值为s = 10  如果fx = 100 Camera.bf = 20
那么 ThDepth = s*fx/Camera.bf = 10 *100 /20 = 50
将你自己的参数带入上述公式 可以得到大概的阈值。
//
#--------------------------------------------------------------------------------------------
# Stereo Rectification. Only if you need to pre-rectify the images.
# Camera.fx, .fy, etc must be the same as in LEFT.P
#--------------------------------------------------------------------------------------------
LEFT.height: 480
LEFT.width: 752
//
调整为你自己的相机大小
//
LEFT.D: !!opencv-matrix
   rows: 1
   cols: 5
   dt: d
   data:[-0.28340811, 0.07395907, 0.00019359, 1.76187114e-05, 0.0]
   //
   位于intrinsics.yml中的
   cameraDistcoeffL: !!opencv-matrix
   rows: 5
   cols: 1
   dt: d
   data: [ -2.8632659642339481e-01, 6.6994801733091039e-02,
       -5.4763802000265397e-04, -1.4767993829858197e-03,
       -6.1039950504068767e-03 ]
       填入上面的 LEFT.D: 即可 左图像畸变参数
   //
LEFT.K: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [458.654, 0.0, 367.215, 0.0, 457.296, 248.375, 0.0, 0.0, 1.0]
   //
   左图像相机内参,可在intrinsics.yml 的cameraMatrixL:找到:
cameraMatrixL: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 2.8424872262658977e+02, 0., 3.3099977082276723e+02, 0.,
       2.8535010886794362e+02, 2.5230877864759117e+02, 0., 0., 1. ]
填入LEFT.K:

   //
LEFT.R:  !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [0.999966347530033, -0.001422739138722922, 0.008079580483432283, 0.001365741834644127, 0.9999741760894847, 0.007055629199258132, -0.008089410156878961, -0.007044357138835809, 0.9999424675829176]
   //
   左相机旋转矩阵:extrinsics.yml 中的 Rl:
Rl: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 9.9750705548699170e-01, 3.5207065558213610e-02,
       6.1156657760632900e-02, -3.5691910468923047e-02,
       9.9933934145707581e-01, 6.8533308118298173e-03,
       -6.0874968425042433e-02, -9.0190437917577089e-03,
       9.9810465136093429e-01 ]
填入上面的LEFT.R: 

   //
LEFT.P:  !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [435.2046959714599, 0, 367.4517211914062, 0,  0, 435.2046959714599, 252.2008514404297, 0,  0, 0, 1, 0]
//
投影矩阵:
extrinsics.yml 中的 Pl:
Pl: !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [ 2.8559499458758660e+02, 0., 2.7029193305969238e+02, 0., 0.,
       2.8559499458758660e+02, 2.8112063348293304e+02, 0., 0., 0., 1.,
       0. ]
填入上面的  LEFT.P:
下面的右侧相机参数配置同上述左侧相机参数配置  orb特征点的参数此处不做叙述。
//

RIGHT.height: 480
RIGHT.width: 752
RIGHT.D: !!opencv-matrix
   rows: 1
   cols: 5
   dt: d
   data:[-0.28368365, 0.07451284, -0.00010473, -3.555907e-05, 0.0]
RIGHT.K: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [457.587, 0.0, 379.999, 0.0, 456.134, 255.238, 0.0, 0.0, 1]
RIGHT.R:  !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [0.9999633526194376, -0.003625811871560086, 0.007755443660172947, 0.003680398547259526, 0.9999684752771629, -0.007035845251224894, -0.007729688520722713, 0.007064130529506649, 0.999945173484644]
RIGHT.P:  !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [435.2046959714599, 0, 367.4517211914062, -47.90639384423901, 0, 435.2046959714599, 252.2008514404297, 0, 0, 0, 1, 0]
//
RIGHT相机的设置与LEFT一致,唯一不同的就是RIGHT.P: 参数,
extrinsics.yml 中的 Pr:如下:
Pr: !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [ 2.8559499458758660e+02, 0., 2.7029193305969238e+02,
       -3.9636548646706200e+04, 0., 2.8559499458758660e+02,
       2.8112063348293304e+02, 0., 0., 0., 1., 0. ]
对其进行修改,也就是data中的第4个值,需要转化单位从mm转为m。
所以应该填入RIGHT.P: 的数值为:
   data: [ 2.8559499458758660e+02, 0., 2.7029193305969238e+02,
       -3.9636548646706200e+01, 0., 2.8559499458758660e+02,
       2.8112063348293304e+02, 0., 0., 0., 1., 0. ]
ORB Parameter 没什么争议,较为明了,暂不介绍。
//
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------

# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1200

# 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

你可能感兴趣的:(SLAM,orbslam,SLAM,双目广角相机标定,orb_slam2)