我正在尝试使用OpenCV通过SIFT功能跟踪，FLANN匹配以及基本矩阵和基本矩阵的后续计算来估计摄像机相对于另一摄像机的一个姿势。分解基本矩阵后，我检查退化的配置并获得“正确的” R和t。
问题是，他们似乎永远都不对。我包括几个图像对:
图像对
结果
图像对
结果
在第二种情况下，平移矢量似乎高估了Y方向的运动，而低估了X方向的运动。将旋转矩阵转换为Euler角时，在两种情况下均得出错误的结果。许多其他数据集也会发生这种情况。我尝试过在RANSAC，LMEDS等之间切换基本矩阵计算技术，现在使用RANSAC进行此操作，并且仅使用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。对极线似乎是正确的，并且基本矩阵满足x'.F.x = 0
我在这里根本错过了什么错误吗？假设程序正确理解了对极几何，那么可能发生什么导致完全错误的姿势？我正在检查以确保点都位于两个摄像头的前面。任何想法/建议都将非常有帮助。谢谢!
编辑:尝试使用相同技术，将两个不同的校准摄像机间隔开；并将基本矩阵计算为K2'.F.K1，但平移和旋转仍然相去甚远。
Code供引用
import cv2
import numpy as np
from matplotlib import pyplot as plt
# K2 = np.float32([[1357.3, 0, 441.413], [0, 1355.9, 259.393], [0, 0, 1]]).reshape(3,3)
# K1 = np.float32([[1345.8, 0, 394.9141], [0, 1342.9, 291.6181], [0, 0, 1]]).reshape(3,3)
# K1_inv = np.linalg.inv(K1)
# K2_inv = np.linalg.inv(K2)
K = np.float32([3541.5, 0, 2088.8, 0, 3546.9, 1161.4, 0, 0, 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[0]*rot[2, :], 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,pts1,pts2):
color = tuple(np.random.randint(0,255,3).tolist())
x0,y0 = map(int, [0, -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, color, -1)
cv2.circle(img2,tuple(pt2), 10,color,-1)
return img1,img2
img1 = cv2.imread('C:\\Users\\Sai\\Desktop\\room1.jpg', 0)
img2 = cv2.imread('C:\\Users\\Sai\\Desktop\\room0.jpg', 0)
img1 = cv2.resize(img1, (0,0), fx=0.5, fy=0.5)
img2 = cv2.resize(img2, (0,0), fx=0.5, 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, mask = cv2.findFundamentalMat(pts1,pts2,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,img2,lines1,pts1,pts2)
# drawing lines on right image
lines2 = cv2.computeCorrespondEpilines(pts1.reshape(-1,1,2), 1,F)
lines2 = lines2.reshape(-1,3)
img3,img4 = drawlines(img2,img1,lines2,pts2,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, 0.0, 0.0, 0.0, 0.0, 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, second_inliers, R, 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, second_inliers, R, 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()
最佳答案
有很多事情可能导致从点对应关系估计相机姿态不准确。您必须考虑的一些因素:
(*)8点方法使代数误差(x'.F.x = 0)最小化。通常最好找到最小化有意义的几何误差的解决方案。例如，您可以在RANSAC实现中使用重新投影错误。
(*)从8个点求解基本矩阵的线性算法对噪声敏感。亚像素准确的点匹配，正确的数据归一化和准确的相机校准对于获得更好的结果都很重要。
(*)特征点定位和匹配会导致噪声点匹配，因此，应将通过求解代数方程x'Fx所获得的解决方案真正用作初始估计，并且需要应用诸如参数优化之类的进一步步骤来完善解决方案。
(*)某些两个 View 的相机配置可能导致解决方案不明确，因此需要其他方法(例如第三 View 消除歧义)来获得可靠的结果。