Robust vision-aided inertial navigation algorithm via entropy-like relative pose estimation

Abstract

The paper presents a loosely coupled approach for the improvement of state estimation in autonomous inertial navigation, augmented via image-based relative motion estimation. The proposed approach uses a novel pose estimation technique based on the minimization of an entropy-like cost function which is robust by nature to the presence of noise and outliers in the visual features. Experimental evidence of the performance of this approach is given and compared to a state-of-the-art algorithm. An indirect Kalman filter is used for navigation in the framework of stochastic cloning. The robust relative pose estimation given by our novel technique is used to feed a relative position fix to the navigation filter. The simulation results are presented and compared with the results obtained via the classical RANSAC-based direct linear transform approach.