This article proposes a novel solution to the Pose Estimation problem for Ego-Motion from stereo camera images. The approach uses a nonlinear function, derived from the concept of Gibbs' Entropy, which is robust by nature to the presence of noise and outliers in the visual features. The SIFT algorithm is used to collect and match the features from stereo images. The 3-vectors quaternion parameterization is used to parameterize the rotation matrix, in order to avoid the unit norm constraint in the minimization computation. Simulations and experimental results are presented and compared with the results obtained via the classical Iterative Closest Point approach.