我检测了ArUco标记并估计了姿势。见下图。但是，我得到的Xt（X平移）是正值。根据drawAxis函数，正方向远离图像中心。所以我认为它应该是负值。为什么我得到的是正值。
我的相机距离成像表面约120 mm。但我得到的Zt（Z平移）在650 mm范围内。位姿估计是否给出了标记相对于物理相机或图像平面中心的位姿？我不明白Zt为什么这么高。
我在改变Z的同时不断测量姿势，并获得了滚转、俯仰、偏航。我注意到滚转（旋转w.r.t.凸轮X轴）的符号在幅度变化166-178之间来回变化，但Xt的符号并没有随着滚转的符号变化而变化。有什么想法吗？有什么建议可以获得更一致的数据吗？

image=cv.imread(fname)
arucoDict = cv.aruco.Dictionary_get(cv.aruco.DICT_4X4_1000)
arucoParams = cv.aruco.DetectorParameters_create()
(corners, ids, rejected) = cv.aruco.detectMarkers(image, arucoDict,
    parameters=arucoParams)

    print(corners, ids, rejected)
    
    if len(corners) > 0:
        # flatten the ArUco IDs list
        ids = ids.flatten()
        # loop over the detected ArUCo corners
        #for (markerCorner, markerID) in zip(corners, ids):
        #(markerCorner, markerID)=(corners, ids)
            # extract the marker corners (which are always returned in
            # top-left, top-right, bottom-right, and bottom-left order)
        #corners = corners.reshape((4, 2))
        (topLeft, topRight, bottomRight, bottomLeft) = corners[0][0][0],corners[0][0][1],corners[0][0][2],corners[0][0][3]
            # convert each of the (x, y)-coordinate pairs to integers
        topRight = (int(topRight[0]), int(topRight[1]))
        bottomRight = (int(bottomRight[0]), int(bottomRight[1]))
        bottomLeft = (int(bottomLeft[0]), int(bottomLeft[1]))
        topLeft = (int(topLeft[0]), int(topLeft[1]))
    
            # draw the bounding box of the ArUCo detection
        cv.line(image, topLeft, topRight, (0, 255, 0), 2)
        cv.line(image, topRight, bottomRight, (0, 255, 0), 2)
        cv.line(image, bottomRight, bottomLeft, (0, 255, 0), 2)
        cv.line(image, bottomLeft, topLeft, (0, 255, 0), 2)
            # compute and draw the center (x, y)-coordinates of the ArUco
            # marker
        cX = int((topLeft[0] + bottomRight[0]) / 2.0)
        cY = int((topLeft[1] + bottomRight[1]) / 2.0)
        cv.circle(image, (cX, cY), 4, (0, 0, 255), -1)
        
        if topLeft[1]!=topRight[1] or topLeft[0]!=bottomLeft[0]:
            rot1=np.degrees(np.arctan((topLeft[0]-bottomLeft[0])/(bottomLeft[1]-topLeft[1])))
            rot2=np.degrees(np.arctan((topRight[1]-topLeft[1])/(topRight[0]-topLeft[0])))
            rot=(np.round(rot1,3)+np.round(rot2,3))/2
            print(rot1,rot2,rot)
        else:
            rot=0
    
        # draw the ArUco marker ID on the image
        rotS=",rotation:"+str(np.round(rot,3))
        cv.putText(image, ("position: "+str(cX) +","+str(cY)),
        (100, topLeft[1] - 15), cv.FONT_HERSHEY_SIMPLEX,0.5, (255, 0, 80), 2)
        cv.putText(image, rotS,
        (400, topLeft[1] -15), cv.FONT_HERSHEY_SIMPLEX,0.5, (255, 0, 80), 2)
        print("[INFO] ArUco marker ID: {}".format(ids))
        
        
        d=np.round((math.dist(topLeft,bottomRight)+math.dist(topRight,bottomLeft))/2,3)
        # Get the rotation and translation vectors
        rvecs, tvecs, obj_points = cv.aruco.estimatePoseSingleMarkers(corners,aruco_marker_side_length,mtx,dst)
        
         
        # Print the pose for the ArUco marker
        # The pose of the marker is with respect to the camera lens frame.
        # Imagine you are looking through the camera viewfinder, 
        # the camera lens frame's:
        # x-axis points to the right
        # y-axis points straight down towards your toes
        # z-axis points straight ahead away from your eye, out of the camera
        #for i, marker_id in enumerate(marker_ids):
         
            # Store the translation (i.e. position) information
        transform_translation_x = tvecs[0][0][0]
        transform_translation_y = tvecs[0][0][1]
        transform_translation_z = tvecs[0][0][2]
               
            # Store the rotation information
        rotation_matrix = np.eye(4)
        rotation_matrix[0:3, 0:3] = cv.Rodrigues(np.array(rvecs[0]))[0]
        r = R.from_matrix(rotation_matrix[0:3, 0:3])
        quat = r.as_quat()   
             
            # Quaternion format     
        transform_rotation_x = quat[0] 
        transform_rotation_y = quat[1] 
        transform_rotation_z = quat[2] 
        transform_rotation_w = quat[3] 
             
            # Euler angle format in radians
        roll_x, pitch_y, yaw_z = euler_from_quaternion(transform_rotation_x,transform_rotation_y,transform_rotation_z,transform_rotation_w)
             
        roll_x = math.degrees(roll_x)
        pitch_y = math.degrees(pitch_y)
        yaw_z = math.degrees(yaw_z)