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Imprecise intrinsic/extrinsic calibration with Aruco markers

asked 2020-04-30 13:49:39 -0500

kastaa gravatar image


I am still new with calibration using OpenCV. I have a setup right now which is composed of three similar cameras (same lens and sensor). Here the following arrangement of the camera, the measurements below are approximate. Rotation between the camera should be approximatively null.

Camera 1->2

  • -380mm (+- 50mm) X axis
  • -290mm (+- 50mm) Y axis
  • -460mm (+- 50mm) Z axis

Camera 1->3

  • -850mm (+- 10mm) X axis
    • 0 mm (+- 50mm) Y axis
    • 0 mm (+- 50mm) Z axis


I did the intrinsic calibration for every camera using the code below. For every intrinsic, the returned error was around 1.5.

import pickle
import cv2
from cv2 import aruco as ar

ar_dict = ar.getPredefinedDictionary(ar.DICT_6X6_1000)
board = ar.GridBoard_create(markersX=6, markersY=9, markerLength=0.04,
                            markerSeparation=0.01, dictionary=ar_dict, firstMarker=108)

criteria = (cv2.TERM_CRITERIA_EPS & cv2.TERM_CRITERIA_COUNT, 10000, 1e-10)

camera_matrix, distortion_coefficients0, _, _ = \
                        cv2.aruco.calibrateCameraAruco(data['corners'], data['ids'], 
                        data['counter'], board, data['imsize'], cameraMatrix=data['cam_init'], 
                        distCoeffs=data['dist_init'],  flags=flags, criteria=criteria)

Here's the output intrinsic I got for camera 1 and 2 respectively. Focal lens of the lens is 2.8mm iwht sensor pixel of 3 microns and sensons dimension of 1208hx1928w:

camera matrix:  [[ 951.48868149    0.          974.18640697]
 [   0.          949.82336112  615.18242086]
 [   0.            0.            1.        ]]
distortion:  [[  2.47752302e-01  -1.35352132e-02   0.00000000e+00   0.00000000e+00
    5.26418500e-04   6.09717199e-01   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00]]

camera matrix:  [[ 964.28616544    0.          961.13919655]
 [   0.          962.12057895  581.30821674]
 [   0.            0.            1.        ]]
distortion:  [[ 0.34504854 -0.02941867  0.          0.          0.00217154  0.69914104
   0.          0.          0.          0.          0.          0.
   0.          0.        ]]

For extrinsic calibration, I am using four Aruco charts as shown in the images below where I colored the detected image points.

image description

Here's the code I am using for extrinsic calibration.

rot = np.eye(3, dtype=np.float64)
essential = np.eye(3, dtype=np.float64)
fondamental = np.eye(3, dtype=np.float64)
tvec = np.zeros((3, 1), dtype=np.float64)


criteria = (cv2.TERM_CRITERIA_EPS & cv2.TERM_CRITERIA_COUNT, 10000, 1e-10)
ret = cv2.stereoCalibrate(ext_calib12['obj_pts'], ext_calib12['img_pts'][0], ext_calib12['img_pts'][1],
                          int_calib1['est_cam_mat'], int_calib1['est_dist'],
                          int_calib2['est_cam_mat'], int_calib2['est_dist'],
                          int_calib1["imsize"], rot, tvec, essential, 
                          fondamental, flags=flags, criteria=criteria)

Then to validate the result of my calibration I am doing the rectification of the image as follow

imsize = int_calib1["imsize"]
R1 = np.eye(3)
R2 = np.eye(3)
Q = np.eye(4)
P1 = np.ones((3, 4))
P2 = np.ones((3, 4))
res = cv2.stereoRectify(int_calib1['est_cam_mat'], int_calib1['est_dist'],
                        int_calib2['est_cam_mat'], int_calib2['est_dist'], imsize, rot,
                        tvec, R1, R2, P1, P2, Q, alpha=-1, flags=cv2.CALIB_ZERO_DISPARITY)

img = ext_calib12['img1']
mapx, mapy = cv2.initUndistortRectifyMap(int_calib1['est_cam_mat'], int_calib1['est_dist'], R1, P1, imsize, 5)
dst1 = cv2.remap(img, mapx, mapy, cv2.INTER_LINEAR)

img = ext_calib12['img2']
mapx, mapy = cv2.initUndistortRectifyMap(int_calib2['est_cam_mat'], int_calib2['est_dist ...
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The issue is that the tags appear too small in the images and too few are detected. You will need to have points that cover a large part of the image.

Try with bigger tags or use regular calibration pattern (chessboard or asymmetric circles grid).

Eduardo gravatar imageEduardo ( 2020-05-01 16:45:41 -0500 )edit

1 answer

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answered 2020-05-06 20:53:48 -0500

kastaa gravatar image

updated 2020-05-06 20:54:58 -0500

After some research, I think I came to answer by myself. There are three elements that I didn't get right.

  1. Tag size and numbers: As pointed out by Eduardo the tags in my image were too small and too few were detected. I took new captures with charts closer to the camera. I ended up with at least 50 tags detected per images and 20 different chart orientations. It helps a little bit, but the calibration results were not that different from the one I got previously.
  2. Image rectification: What was concerning me the most in my question was the rectification between camera 1->2 which was looking completely wrong. Initially, I thought this wrong rectification was caused by an imprecise calibration but it wasn't. Since rectification between 1->3 was not so bad I did a little bit of research and found out that my rectification of 1->2 is probably not working because my epipolar pole is located inside my images. I computed the epipolar lines on my image and indeed my epipole is in my image.
  3. Pose: I assumed the pose between my two cameras to be the identity. However, this is not totally the case, after computing the intrinsic I found the yaw, pitch, and roll of [-4.81677474, 3.30338745, 1.37281632] (degree) between camera 1->3. This explains why I found a tvec with non-zeros value on Y and Z axis between my camera 1->3.
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