Abstract. This paper addresses the problem of robustly autocalibrating a moving camera with constant intrinsics. The proposed calibration method uses the
Branch-and-Bound (BnB) search paradigm to maximize the consensus of the
polynomials. These polynomials are parameterized by the entries of, either the
Dual Image of Absolute Conic (DIAC) or the Plane-at-Infinity (PaI). During the
BnB search, we exploit the theory of sampling algebraic varieties, to test the positivity of any polynomial within a parameter’s interval, i.e. outliers with certainty.
The search process explores the space of exact parameters (i.e the entries of DIAC
or PaI), benefits from the solution of a local method, and converges to the solution satisfied by the largest number of polynomials. Given many polynomials on
the sought parameters (with possibly overwhelmingly many from outlier measurements), their consensus for calibration is searched for two cases: simplified
Kruppa’s equations and Modulus constraints, expressed in DIAC and PaI, resp.
Our approach yields outstanding results in terms of robustness and optimality