1

I am trying to estimate the pose and position of a satellite given an image of it. I have a 3D model of the satellite. Using either PnP solvers or POSIT works great when I pick out the point correspondences myself, however I need to to find a method to match the points up automatically. Using a corner detector (best one I found so far is based on the contour) I can find all the relevant points in the image in addition a few spurious points. However I need to match a given point in the image to the correct point in the 3D model. The articles I have read on the subject always seem to assume that we have found the point pairs without going into details about how to do so.

Is there any approach usually taken that can determine these correspondences based on some invariant features? Or should i resort to a different method not based on corner points?

mnel
  • 113,303
  • 27
  • 265
  • 254
user1851897
  • 11
  • 3

2 Answers2

1

You can have a look at the SoftPOSIT algorithm, which determines 3D-2D correspondences and then executes POSIT algorithm. As far as I know Matlab code is available for SoftPOSIT.

Karmar
  • 570
  • 1
  • 6
  • 15
  • 1
    Thanks for the answer, however I have already tried the softPOSIT algorithm and it is unfortunately not very robust to spurious points, at least not in my application. – user1851897 Dec 13 '12 at 20:18
0

ou have to do PnP with RANSAC, see openCV code solvePnPRansac(). This method can tolerate a high percent of mismatches so you don't need to be precise with all your matches but just have a certain percent of correct ones (even as low as 30%). Of course the min number of right correspondences is 4.

Speaking of invariant features - if the amount of rotation between neighbouring frame is small you don't need to use invariant features. Even a small patch of with grey intensities would suffice to find a match. The only problem is that you have to update your descriptor or even choose a different feature point on your model depending on the model rotation. The latter may be hard to do since you have to know 3D coordinate of every feature.

Vlad
  • 4,425
  • 1
  • 30
  • 39