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I am currently working on pose estimation of one camera with respect to another using opencv, in a setup where camera1 is fixed and camera2 is free to move. I know the intrinsics of both the cameras. I have the pose estimation module using epipolar geometry and computing essential matrix using the five-point algorithm to figure out the R and t of camera2 with respect to camera1; but I would like to get the metric translation. To help achieve this, I have two GPS modules, one on camera1 and one on camera2. For now, if we assume camera1's GPS is flawless and accurate; camera2's GPS exhibits some XY noise, I would need a way to use the opencv pose estimate on top of this noisy GPS to get the final accurate translation.

Given that info, my question has two parts:

  1. Because the extrinsics between the cameras keep changing, would it be possible to use bundle adjustment to refine my pose?
  2. And can I somehow incorporate my (noisy) GPS measurements in a bundle adjustment framework as an initial estimate, and obtain a more accurate estimate of metric translation as my end result?
HighVoltage
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1) No, bundle adjustment has another function and you would not be able to work with it anyway because you would have an unknown scale for every pair you use with 5-point. You should instead use a perspective-n-point algorithm after the first pair of images.

2) Yes, it's called sensor fusion and you need to first calibrate (or know) the transformation between your GPS sensor coordinates and your camera coordinates. There is an open source framework you can use.

aledalgrande
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  • Thank you! So, correct me if I am wrong; once I obtain my R1 and t1 (say) with the first pair, I use something like cv::triangulatePoints to get the 3D points, and then pass these and the image points to the PnP algorithm with the R1 and t1 as initial estimates? And I am assuming this works on a frame-by-frame basis? – HighVoltage Aug 06 '15 at 04:17
  • Yes, correct. Also, it's not mandatory to pass the initial estimate. – aledalgrande Aug 06 '15 at 04:18