Uncertain geometry with dependencies

Classical computational geometry algorithms handle geometric constructs whose shapes and locations are exact. However, many real-world applications require computing with geometric uncertainties, which are often coupled and mutually dependent. Existing uncertainty models cannot be used to handle dependencies among objects resulting in overestimation of the mutual errors. We have recently developed the Linear Parametric Geometric Uncertainty Model (LPGUM), a general and computationally efficient worst-case first-order linear approximation of geometric uncertainty that supports dependencies among uncertainties. In this paper, we present the properties of the uncertainty ones of a point and a line, and offer efficient algorithms to compute them. We also describe new efficient algorithms to handle relative position queries, e.g., the classification of an uncertain point with respect to an uncertain line. We show that, in all cases, the overhead of computing with dependent uncertainties is low.