I'm trying to develop a quality measurement (kind of a metric) for the
quality of given point cloud data (coming from a 3D LiDAR) at a given point or region. It should come into account when there are situations where LiDAR data is not as usable as we want it to. Imagine a very foggy and/or rainy environment for example, where the rain/fog particles cause scattering.
So one approach I am thinking of is to observe the x- and y-position at the "position of interest" for a while, take the regarding z-values during this time-slot and compute the variance (considering the data is normally distributed).
Am I right in saying that if this variance exceeds some threshold, I could interpret this as "bad"/unreliable point cloud data at this point?
Of course, this only holds for static regions - if I have dynamically changed "regions/points of interest" obviously the variance will be no meaningful metric.