Merge branch 4.x

doc/opencv.bib

@@ -769,6 +769,22 @@
   number = {8},
   publisher = {IOP Publishing Ltd}
 }
+@article{Lourakis2009_sba,
+  author = {Lourakis, Manolis I. A. and Argyros, Antonis A.},
+  title = {SBA: A Software Package for Generic Sparse Bundle Adjustment},
+  year = {2009},
+  month = mar,
+  journal = {ACM Transactions on Mathematical Software},
+  volume = {36},
+  number = {1},
+  articleno = {2},
+  pages = {2:1--2:30},
+  numpages = {30},
+  publisher = {Association for Computing Machinery},
+  doi = {10.1145/1486525.1486527},
+  url = {https://scispace.com/pdf/sba-a-software-package-for-generic-sparse-bundle-adjustment-1d4hp0z31z.pdf},
+  month_numeric = {3}
+}
 @article{LowIlie2003,
   author   = {Kok-Lim Low, Adrian Ilie},
   year = {2003},
@@ -1253,6 +1269,20 @@
   publisher = {Taylor \& Francis},
   url = {https://www.olivier-augereau.com/docs/2004JGraphToolsTelea.pdf}
 }
+@incollection{Triggs2000_bundle_adjustment,
+  author = {Triggs, Bill and McLauchlan, Philip F. and Hartley, Richard I. and Fitzgibbon, Andrew W.},
+  title = {Bundle Adjustment---A Modern Synthesis},
+  booktitle = {Vision Algorithms: Theory and Practice},
+  year = {2000},
+  pages = {298--372},
+  publisher = {Springer},
+  series = {Lecture Notes in Computer Science},
+  volume = {1883},
+  editor = {Triggs, Bill and Zisserman, Andrew and Szeliski, Richard},
+  doi = {10.1007/3-540-44480-7_21},
+  isbn = {978-3-540-67973-8},
+  url = {https://www.cs.jhu.edu/~misha/ReadingSeminar/Papers/Triggs00.pdf}
+}
 @article{Tsai89,
   author = {R. Y. Tsai and R. K. Lenz},
   journal = {IEEE Transactions on Robotics and Automation},

doc/py_tutorials/py_calib3d/py_calibration/py_calibration.markdown

@@ -206,17 +206,17 @@ Re-projection Error
 
 Re-projection error gives a good estimation of just how exact the found parameters are. The closer the re-projection error is to zero, the more accurate the parameters we found are. Given the intrinsic, distortion, rotation and translation matrices,
 we must first transform the object point to image point using **cv.projectPoints()**. Then, we can calculate
-the absolute norm between what we got with our transformation and the corner finding algorithm. To
-find the average error, we calculate the arithmetical mean of the errors calculated for all the
-calibration images.
+the norm between what we got with our transformation and the corner finding algorithm. To find the
+RMSE (root mean squared error), we average the squared errors over all points and images, then take
+the square root.
 @code{.py}
 mean_error = 0
 for i in range(len(objpoints)):
     imgpoints2, _ = cv.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
-    error = cv.norm(imgpoints[i], imgpoints2, cv.NORM_L2)/len(imgpoints2)
+    error = cv.norm(imgpoints[i], imgpoints2, cv.NORM_L2SQR) / len(imgpoints2)
     mean_error += error
 
-print( "total error: {}".format(mean_error/len(objpoints)) )
+print( "total error: {}".format(np.sqrt(mean_error/len(objpoints))) )
 @endcode
 
 Exercises