我一直在努力从Essential矩阵中正确解释recoverPose的结果，这让我头疼不已。这是基于OpenCV中我如何估计两个摄像头的位置？
以下是我正在使用的高级步骤：1.检测两张图像中的ORB特征2.使用BFMatcher匹配特征3.在两个图像中查找Essential4.从两张图像中恢复姿势，即R、T5.使用R、T将好的特征(从恢复姿势中屏蔽)三角化，以创建三维点云(地标)6.作为一个基本事实，我还从图像中提取棋盘角，并使用上面计算的R、T对其进行三角测量。棋盘角的良好平面形状表明R、T对于三角测量是准确的。7.绘制所有
import numpy as np
import cv2
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def plot_pose3_on_axes(axes, gRp, origin, axis_length=0.1):
"""Plot a 3D pose on given axis 'axes' with given 'axis_length'."""
# get rotation and translation (center)
#gRp = pose.rotation().matrix() # rotation from pose to global
#t = pose.translation()
#origin = np.array([t.x(), t.y(), t.z()])
# draw the camera axes
x_axis = origin + gRp[:, 0] * axis_length
line = np.append(origin, x_axis, axis=0)
axes.plot(line[:, 0], line[:, 1], line[:, 2], 'r-')
y_axis = origin + gRp[:, 1] * axis_length
line = np.append(origin, y_axis, axis=0)
axes.plot(line[:, 0], line[:, 1], line[:, 2], 'g-')
z_axis = origin + gRp[:, 2] * axis_length
line = np.append(origin, z_axis, axis=0)
axes.plot(line[:, 0], line[:, 1], line[:, 2], 'b-')
img1 = cv2.imread('/home/vik748/data/chess_board/GOPR1488.JPG',1) # queryImage
img2 = cv2.imread('/home/vik748/data/chess_board/GOPR1490.JPG',1)
fx = 3551.342810
fy = 3522.689669
cx = 2033.513326
cy = 1455.489194
K = np.float64([[fx, 0, cx],
[0, fy, cy],
[0, 0, 1]])
D = np.float64([-0.276796, 0.113400, -0.000349, -0.000469]);
print(K,D)
# Convert images to greyscale
gr1=cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
gr2=cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
#Initiate ORB detector
detector = cv2.ORB_create(nfeatures=25000, edgeThreshold=15, patchSize=125, nlevels=32,
fastThreshold=20, scaleFactor=1.2, WTA_K=2,
scoreType=cv2.ORB_HARRIS_SCORE, firstLevel=0)
# find the keypoints and descriptors with ORB
kp1, des1 = detector.detectAndCompute(gr1,None)
kp2, des2 = detector.detectAndCompute(gr2,None)
print ("Points detected: ",len(kp1), " and ", len(kp2))
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1,des2)
kp1_match = np.array([kp1[mat.queryIdx].pt for mat in matches])
kp2_match = np.array([kp2[mat.trainIdx].pt for mat in matches])
kp1_match_ud = cv2.undistortPoints(np.expand_dims(kp1_match,axis=1),K,D)
kp2_match_ud = cv2.undistortPoints(np.expand_dims(kp2_match,axis=1),K,D)
E, mask_e = cv2.findEssentialMat(kp1_match_ud, kp2_match_ud, focal=1.0, pp=(0., 0.),
method=cv2.RANSAC, prob=0.999, threshold=0.001)
print ("Essential matrix: used ",np.sum(mask_e) ," of total ",len(matches),"matches")
points, R, t, mask_RP = cv2.recoverPose(E, kp1_match_ud, kp2_match_ud, mask=mask_e)
print("points:",points,"trecover pose mask:",np.sum(mask_RP!=0))
print("R:",R,"t:",t.T)
bool_mask = mask_RP.astype(bool)
img_valid = cv2.drawMatches(gr1,kp1,gr2,kp2,matches, None,
matchColor=(0, 255, 0),
matchesMask=bool_mask.ravel().tolist(), flags=2)
plt.imshow(img_valid)
plt.show()
ret1, corners1 = cv2.findChessboardCorners(gr1, (16,9),None)
ret2, corners2 = cv2.findChessboardCorners(gr2, (16,9),None)
corners1_ud = cv2.undistortPoints(corners1,K,D)
corners2_ud = cv2.undistortPoints(corners2,K,D)
#Create 3 x 4 Homogenous Transform
Pose_1 = np.hstack((np.eye(3, 3), np.zeros((3, 1))))
print ("Pose_1: ", Pose_1)
Pose_2 = np.hstack((R, t))
print ("Pose_2: ", Pose_2)
# Points Given in N,1,2 array
landmarks_hom = cv2.triangulatePoints(Pose_1, Pose_2,
kp1_match_ud[mask_RP[:,0]==1],
kp2_match_ud[mask_RP[:,0]==1]).T
landmarks_hom_norm = landmarks_hom / landmarks_hom[:,-1][:,None]
landmarks = landmarks_hom_norm[:, :3]
corners_hom = cv2.triangulatePoints(Pose_1, Pose_2, corners1_ud, corners2_ud).T
corners_hom_norm = corners_hom / corners_hom[:,-1][:,None]
corners_12 = corners_hom_norm[:, :3]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_aspect('equal') # important!
title = ax.set_title('3D Test')
ax.set_zlim3d(-5,10)
# Plot triangulated featues in Red
graph, = ax.plot(landmarks[:,0], landmarks[:,1], landmarks[:,2], linestyle="", marker="o",color='r')
# Plot triangulated chess board in Green
graph, = ax.plot(corners_12[:,0], corners_12[:,1], corners_12[:,2], linestyle="", marker=".",color='g')
# Plot pose 1
plot_pose3_on_axes(ax,np.eye(3),np.zeros(3)[np.newaxis], axis_length=0.5)
#Plot pose 2
plot_pose3_on_axes(ax, R, t.T, axis_length=1.0)
ax.set_zlim3d(-2,5)
ax.view_init(-70, -90)
plt.show()
因此，正如我们从图像1488和1490中看到的那样，相机向左上移动，并向下和向右指向。然而，第二个位置的R和T的情节反映了完全不同的东西。
图像匹配结果
相机姿态图
我试过用R'和-(R')*T来反转，但这也不正确。我试过很多不同的组合，但似乎都没有意义。
那是什么呢？？？
python脚本和测试图像可以在这里找到。
我通过取旋转矩阵的倒数来解决这个问题，因为recoverPose函数定义的是点移动方向的旋转和平移，而不是相机移动方向。有关更多信息，请参阅此帖子。
为什么在cv2.findEssentialMat中使用focal = 1.0和pp=(0,0)，当您已经在相机矩阵'K'中定义了fx、fy、cx、cy时。据我所知，cv2.findEssentialMat中应该使用相同的fx、fy、cx和cy或'K'本身。像这样的东西。如果我错了，请向我解释。我也在解决同样的问题。
E, mask_e = cv2.findEssentialMat(kp1_match_ud, kp2_match_ud, cameraMatrix=K, method=cv2.RANSAC, prob=0.999, threshold=0.001)