从多张照片中的棋盘格网格估计相机姿势时确保共享原点

如何解决从多张照片中的棋盘格网格估计相机姿势时确保共享原点

我有两张静止场景的照片，其中包括棋盘格图案和瓶子。我的目标是估计相对于共享原点的相机姿势，以便我可以执行进一步的处理。我最初的计划是使用棋盘格来估计相机位置。我对这个过程感到担忧，因为棋盘格的原点在两个图像中可能不同。即，由于棋盘图案的对称性，被选为棋盘图案原点的点是否可能因照片而异。如果是这样，无论如何要避免这种情况，同时仍然使用棋盘格网格进行姿势估计。

下面给出了我正在使用的两个图像作为参考

我正在使用下面的代码推导出我的相机的姿势

################### CORNER FINDING CRITERIA ####################
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER,30,0.001)

################### GENERATE THE OBJECT POINTS #################
squareSZ = 23.876 #Square edge length in mm
dim1 = 9
dim2 = 6
objp = np.zeros((dim2*dim1,3),np.float32) #Generates object points 
objp[:,:2] = np.mgrid[0:dim1,0:dim2].T.reshape(-1,2)*squareSZ #Generates a 3d grid from object points

################### LOAD CAMERA EXTRINSICS #####################
camMat = np.array(pickle.load(open('cameraMatrix_GoPro.sav','rb'))) #Loads the camera matrix from the calibration
distCoeffs = np.array(pickle.load(open('distCoeff_GoPro.sav','rb'))) #Loads the distortion coefficients from calibration


################### LOAD CAMERA IMAGES #########################
file1 = 'WaterBottleTooSide/GOPR0250.JPG'
file2 = 'WaterBottleTooSide/GOPR0251.JPG'

# Loads Images
img1 = cv.imread(file1)
img2 = cv.imread(file2)

# Converts to grayscale
img1Gray = cv.cvtColor(img1,cv.COLOR_BGR2GRAY)
img2Gray = cv.cvtColor(img2,cv.COLOR_BGR2GRAY)

################### CALCULATE CAMERA EXTRINSICS #################
ret1,camCorners1 = cv.findChessboardCorners(img1Gray,(dim1,dim2),None) # Find the chess board corners
ret2,camCorners2 = cv.findChessboardCorners(img2Gray,None)

if ret1 == True:
    #Iterates to find the sub-pixel accurate location of corners or radial saddle points
    camPoints1 = np.squeeze(cv.cornerSubPix(img1Gray,camCorners1,(11,11),(-1,-1),criteria))
    worldPoints=np.squeeze(np.array(objp)) #Points from my world frame

    retval,rvecs1,tvecs1 = cv.solvePnP(worldPoints,camPoints1,camMat,distCoeffs) #Calculates location using camera and world view points
    rotMat1 = cv.Rodrigues(rvecs1)[0] #Calculates rotation matrix from the rvec
    R_t1 = np.hstack((rotMat1,tvecs1)) #Calculates the 3x4 matrix [R|t]
    P1 = np.matmul(camMat,R_t1) #Calculates the projection matrix
    P1inv = np.linalg.pinv(P1) #Calculate the pseudo-inverse of the projection matrix 4x3 matrix
    C1 = np.matmul(-np.transpose(rotMat1),tvecs1) #Location of camera center for first image
    C1 = np.vstack((C1,1))#Concatenates 1 to create homogenous point

if ret2 == True:
    #Iterates to find the sub-pixel accurate location of corners or radial saddle points
    camPoints2 = np.squeeze(cv.cornerSubPix(img2Gray,camCorners2,rvecs2,tvecs2 = cv.solvePnP(worldPoints,camPoints2,distCoeffs) #Calculates location using camera and world view points
    rotMat2 = cv.Rodrigues(rvecs2)[0] #Calculates rotation matrix from the rvec
    R_t2 = np.hstack((rotMat2,tvecs2)) #Calculates the 3x4 matrix [R|t]
    P2 = np.matmul(camMat,R_t2) #Calculates the projection matrix
    P2inv = np.linalg.pinv(P2) #Calculate the pseudo-inverse of the projection matrix 4x3 matrix
    C2 = np.matmul(-np.transpose(rotMat2),tvecs2) #Location of camera center for first image
    C2 = np.vstack((C2,1))#Concatenates 1 to create homogenous point