Passive navigation using egomotion estimates

Abstract The goal of this work is to propose a method to solve the problem of passive navigation with visual means. Passive navigation is the ability of an autonomous agent to determine its motion with respect to the environment. The two main egomotion parameters allowing performing passive navigation are the heading direction and the time to collision with the environment. A lot of approaches have been proposed in literature in order to estimate the above parameters, most of which work well only if the motion is a predominant forward translation and small amounts of noise are present in the input data. The method we propose is a two-state approach: matching of features extracted from 2D images of a sequence at different times and egomotion parameter computation. Both algorithms are based on optimization approaches minimizing appropriate energy functions. The novelty of the proposed approach is to formulate the matching energy function in order to englobe invariant cues of the scene. The matching stage recovers correspondences between sparse high interest feature points of two successive images useful to perform the second stage of egomotion parameter estimation. Experimental results obtained in real context show the robustness of the method.