An aerial vehicle captures images of the ground using its down facing camera. From the images, multiple targets are converted from their pixel position to the camera reference frame using the pinhole camera model. Since the targets are static and there is information of the vehicle attitude and orientation, each sample is then converted to the world referencial frame. Note that all targets are on a flat, level plane.
The vehicle keeps "scanning" for the targets and converting them to the world referencial frame. Due to the quality of the camera and detection algorithm, as well as errors on the altitude information, the position of the "scanned" targets is not constant (not accurate). A good representation might be a gaussian distribution around the target true position, however it will also be influenced by the movement of the aerial vehicle.
What's the best approach to estimate the position of the targets from multiple readings? This basically resumes to a problem of noise removal (as well as outlier removal) and estimation, so I would like to know what algorithms and strategies could solve the problem. In the end I expect to implement and test a collection of different approachs to understand their performance on this specific problem.
Furthermore, this system is implemented using ROS, so if you know of packages that already do what I'm searching for I would be glad to hear. You can also cite papers on the topic that you think might be of my interest.