Abstract
We present an algorithmic solution to the robustness problem in computational geometry, called controlled linear perturbation, and demonstrate it on Minkowski sums of polyhedra. The robustness problem is how to implement real RAM algorithms accurately and efficiently using computer arithmetic. Approximate computation in floating point arithmetic is efficient but can assign incorrect signs to geometric predicates, which can cause combinatorial errors in the algorithm output. We make approximate computation accurate by performing small input perturbations, which we compute using differential calculus. This strategy supports fast, accurate Minkowski sum computation. The only prior robust implementation uses a less efficient algorithm, requires exact algebraic computation, and is far slower based on our extensive testing.
► We present a robustness algorithm called controlled linear perturbation (CLP). ► CLP computes a small input perturbation that makes the output correct.► CLP computes the perturbation efficiently using differential calculus.► CLP supports the first robust convolution algorithm for Minkowski sums of polyhedra.
