Error propagation in 3D-from-2D recognition: Scaled-orthographic and perspective projections

Robust recognition systems require a careful under-standing of the e ects of error in sensed features.
Error in these image features results in uncertainty
in the possible image location of each additional
model feature. We present an accurate, analytic ap-
proximation for this uncertainty when model poses
are based on matching three image and model points.
This result applies to objects that are fully three-
dimensional, where past results considered only two-
dimensional objects. Further, we introduce a lin-
ear programming algorithm to compute this uncer-
tainty when poses are based on any number of initial
matches.