I am currently working on a pose estimation problem for which I would like to use filtering. To explain the system briefly, it consists of two cameras and each has its own GPS/IMU module. The main assumption is that Camera1 is fixed and stable, whereas camera2 has a noisy pose in 3D. I am using computer vision to obtain the pose (metric translation and rotation) of camera2 w.r.t. camera1, so that I can improve upon the inherent noise of GPS/IMU modules.
The problem here is that the translation obtained through the vision method is only up to an arbitrary scale, i.e. at any given instant, I can only obtain a unit vector that specifies the "direction" of the translation and not absolute metric translation. The camera based estimation, although accurate, has no idea about how much actual distance is between the cameras, which is why I have the GPS, which gives me position data with some noise.
Example: camera 2 is 5 m to the east of camera 1, the pose from my vision algorithm would say [1, 0, 0] ; 1 m north-east to camera 1, it would be something like [0.7, 0.7, 0]
Hence, would it be possible to consider the GPS estimate of the metric translation as well as its covariance ellipse, and somehow link it with the normalized camera measurements to obtain a final, more accurate estimate of metric translation? I am not sure what kind of filters would be happy to use a measurement that has no absolute value in it.