Robust Algorithms for Ob ject Lo calizati on y Dinesh Mano cha Aaron S. Wallack Department of Computer Science Computer Science Division University of North Carolina University of California Chap el Hill, NC 27599-3175 Berkeley, CA 94720 mano
[email protected] wallack@rob otics.eecs.b erkeley.edu Abstract Ob ject lo calization using sensed data features and corresp onding mo del features is a fun- damental problem in machine vision. We reformulate ob ject lo calization as a least squares problem: the optimal p ose estimate minimizes the squared error discrepancy b etween the sensed and predicted data. The resulting problem is non-linear and previous attempts to estimate the optimal p ose using lo cal metho ds such as gradient descent su er from lo cal minima and, at times, return incorrect results. In this pap er, we describ e an exact, accurate and ecient algorithm based on resultants, linear algebra, and numerical analysis, for solv- ing the nonlinear least squares problem asso ciated with lo calizing two-dimensional ob jects given two-dimensional data. This work is aimed at tasks where the sensor features and the mo del features are of di erenttyp es and where either the sensor features or mo del features are p oints. It is applicable to lo calizing mo deled ob jects from image data, and estimates the p ose using all of the pixels in the detected edges. The algorithm's running time dep ends mainly on the typ e of non-p oint features, and it also dep ends to a small extent on the number of features.