python – 极线几何姿态估计：极线看起来不错,但姿势错误

我正在尝试使用OpenCV来估计相机相对于另一个的一个姿势,使用SIFT特征跟踪,FLANN匹配以及基本和基本矩阵的后续计算.在分解基本矩阵之后,我检查退化配置并获得“右”R和t.

问题是,它们似乎永远不对.我包括几个图像对：

>图像2沿Y轴旋转45度,相同位置w.r.t.图片1.

图像对

结果

>图片2取自约.沿负X方向几米远,在负Y方向轻微位移.约.沿Y轴在相机中旋转45-60度.

图像对

结果

第二种情况下的平移向量似乎高估了Y中的运动并低估了X中的运动.转换为欧拉角时的旋转矩阵在两种情况下都给出了错误的结果.许多其他数据集也会发生这种情况.我已经尝试在RANSAC,LMEDS等之间切换基本矩阵计算技术,现在我正在使用RANSAC和仅使用8点法的内点进行第二次计算.更改特征检测方法也无济于事.极线看起来是正确的,基本矩阵满足x’.F.x = 0

我在这里错过了一些根本错误的东西吗？鉴于程序正确理解极线几何,可能会发生什么导致完全错误的姿势？我正在检查以确保点位于两个摄像头前.任何想法/建议都会非常有帮助.谢谢！

编辑：尝试相同的技术与两个不同的校准相机分开;并计算必要矩阵为K2′.F.K1,但翻译和旋转仍然是关闭的.

Code供参考

import cv2
import numpy as np

from matplotlib import pyplot as plt

# K2 = np.float32([[1357.3,441.413],[0,1355.9,259.393],1]]).reshape(3,3)
# K1 = np.float32([[1345.8,394.9141],1342.9,291.6181],3)

# K1_inv = np.linalg.inv(K1)
# K2_inv = np.linalg.inv(K2)

K = np.float32([3541.5,2088.8,3546.9,1161.4,1]).reshape(3,3)
K_inv = np.linalg.inv(K)

def in_front_of_both_cameras(first_points,second_points,rot,trans):
    # check if the point correspondences are in front of both images
    rot_inv = rot
    for first,second in zip(first_points,second_points):
        first_z = np.dot(rot[0,:] - second[0]*rot[2,:],trans) / np.dot(rot[0,second)
        first_3d_point = np.array([first[0] * first_z,second[0] * first_z,first_z])
        second_3d_point = np.dot(rot.T,first_3d_point) - np.dot(rot.T,trans)

        if first_3d_point[2] < 0 or second_3d_point[2] < 0:
            return False

    return True

def drawlines(img1,img2,lines,pts1,pts2):
    ''' img1 - image on which we draw the epilines for the points in img1
        lines - corresponding epilines '''
    pts1 = np.int32(pts1)
    pts2 = np.int32(pts2)
    r,c = img1.shape
    img1 = cv2.cvtColor(img1,cv2.COLOR_GRAY2BGR)
    img2 = cv2.cvtColor(img2,cv2.COLOR_GRAY2BGR)
    for r,pt1,pt2 in zip(lines,pts2):
        color = tuple(np.random.randint(0,255,3).tolist())
        x0,y0 = map(int,-r[2]/r[1] ])
        x1,y1 = map(int,[c,-(r[2]+r[0]*c)/r[1] ])
        cv2.line(img1,(x0,y0),(x1,y1),color,1)
        cv2.circle(img1,tuple(pt1),10,-1)
        cv2.circle(img2,tuple(pt2),-1)
    return img1,img2


img1 = cv2.imread('C:UsersSaiDesktoproom1.jpg',0)  
img2 = cv2.imread('C:UsersSaiDesktoproom0.jpg',0) 
img1 = cv2.resize(img1,(0,0),fx=0.5,fy=0.5)
img2 = cv2.resize(img2,fy=0.5)

sift = cv2.SIFT()

# find the keypoints and descriptors with SIFT
kp1,des1 = sift.detectAndCompute(img1,None)
kp2,des2 = sift.detectAndCompute(img2,None)

# FLANN parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE,trees = 5)
search_params = dict(checks=50)   # or pass empty dictionary

flann = cv2.FlannBasedMatcher(index_params,search_params)

matches = flann.knnMatch(des1,des2,k=2)

good = []
pts1 = []
pts2 = []

# ratio test as per Lowe's paper
for i,(m,n) in enumerate(matches):
    if m.distance < 0.7*n.distance:
        good.append(m)
        pts2.append(kp2[m.trainIdx].pt)
        pts1.append(kp1[m.queryIdx].pt)

pts2 = np.float32(pts2)
pts1 = np.float32(pts1)
F,mask = cv2.findFundamentalMat(pts1,pts2,cv2.FM_RANSAC)

# Selecting only the inliers
pts1 = pts1[mask.ravel()==1]
pts2 = pts2[mask.ravel()==1]

F,cv2.FM_8POINT)

print "Fundamental matrix is"
print 
print F

pt1 = np.array([[pts1[0][0]],[pts1[0][1]],[1]])
pt2 = np.array([[pts2[0][0],pts2[0][1],1]])

print "Fundamental matrix error check: %f"%np.dot(np.dot(pt2,F),pt1)
print " "


# drawing lines on left image
lines1 = cv2.computeCorrespondEpilines(pts2.reshape(-1,1,2),2,F)
lines1 = lines1.reshape(-1,3)
img5,img6 = drawlines(img1,lines1,pts2)

# drawing lines on right image
lines2 = cv2.computeCorrespondEpilines(pts1.reshape(-1,F)
lines2 = lines2.reshape(-1,3)
img3,img4 = drawlines(img2,img1,lines2,pts1)

E = K.T.dot(F).dot(K)

print "The essential matrix is"
print E
print 

U,S,Vt = np.linalg.svd(E)
W = np.array([0.0,-1.0,0.0,1.0,1.0]).reshape(3,3)

first_inliers = []
second_inliers = []
for i in range(len(pts1)):
    # normalize and homogenize the image coordinates
    first_inliers.append(K_inv.dot([pts1[i][0],pts1[i][1],1.0]))
    second_inliers.append(K_inv.dot([pts2[i][0],pts2[i][1],1.0]))

# Determine the correct choice of second camera matrix
# only in one of the four configurations will all the points be in front of both cameras
# First choice: R = U * Wt * Vt,T = +u_3 (See Hartley Zisserman 9.19)

R = U.dot(W).dot(Vt)
T = U[:,2]
if not in_front_of_both_cameras(first_inliers,second_inliers,R,T):

    # Second choice: R = U * W * Vt,T = -u_3
    T = - U[:,2]
    if not in_front_of_both_cameras(first_inliers,T):

        # Third choice: R = U * Wt * Vt,T = u_3
        R = U.dot(W.T).dot(Vt)
        T = U[:,2]

        if not in_front_of_both_cameras(first_inliers,T):

            # Fourth choice: R = U * Wt * Vt,T = -u_3
            T = - U[:,2]

# Computing Euler angles

thetaX = np.arctan2(R[1][2],R[2][2])
c2 = np.sqrt((R[0][0]*R[0][0] + R[0][1]*R[0][1]))

thetaY = np.arctan2(-R[0][2],c2)

s1 = np.sin(thetaX)
c1 = np.cos(thetaX)

thetaZ = np.arctan2((s1*R[2][0] - c1*R[1][0]),(c1*R[1][1] - s1*R[2][1]))

print "Pitch: %f,Yaw: %f,Roll: %f"%(thetaX*180/3.1415,thetaY*180/3.1415,thetaZ*180/3.1415)

print "Rotation matrix:"
print R
print
print "Translation vector:"
print T

plt.subplot(121),plt.imshow(img5)
plt.subplot(122),plt.imshow(img3)
plt.show()