I have a setup in which the orbbec sensor is pointing to a flat surface.
I select 3 random points of this surface and calculate the plane equation in the following form:
Ax+By+Cy+D = 0;
Of which (A, B, C) is the planes normal vector.
Expanding from this i can take any pixel x,y,z in the depth map and calculate its vertical distance to this plane in the following manner:
L = |A * x + B * y + C * z + D| / sqrt(a^2 + b^2 + c^2 )
My problem is, though the result L makes sense (higher objects get higher values), it has a significant error (20-30%). This error wouldn’t matter for many applications, but I do need to have somewhat precise readings. I suspect that this error comes from the fact that I never take into account lens distortion and pixel angular size.
Can someone help me understand how do I incorporate this factor into my calculations?
Just so you are aware - lens distortion is not the only type of error that can occur with a depth sensor.
It is possible you are also seeing depth error reporting - which is a fluctuation from pixel to pixel in depth calculation.
I dont recall orbbec publishing any detailed error ranges - but ive seen anecdotal reports that the errors increase with distance as well … this may mean that any give pixel can be out in its zdepth by up a couple of mm up to even a cm at distances.
Are U measuring single points or the mean average of a group of points … And most structured light style sensors are also notoriously bad at edge … They don’t see round edges of corners very well … This can result in large inaccuracies where the sensor reports garbage.
Westa
No, I’m using a value for each point straight way. My plan is to calculate a rough plane using those 3 points, use this plane to calculate all points that belong to it, and finally use linear regression in the whole list of points to fit the best plane.
But, again, I suspect that I need to account for lens distortion effect.
I found this thread in which this seems to be accounted for, but I’m not sure of how to apply it to my case.
