Mobile robot navigation using egomotion estimates

The main aim of this paper is to describe an efficient approach for egomotion parameters estimation in order to solve the passive navigation problem. Passive navigation is the ability of an autonomous agent to determine its motion with respect to the environment. The two main egomotion parameters allowing to perform 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 translation forward or small amounts of noise are present in the input data. In our work we propose a method computing in real time, using a least square approach, the egomotion parameters from same robust feature correspondences. The approach we propose is based on the observation that the radial structures of the 2D motion field generated by passive navigation can be approximated by a 2D motion field corresponding to the projection on the image plane of a translational motion on both X horizontal axis and Z axis. The aim is to show that the heading direction can be computed correctly from translational motion parameters, though large rotations are performed by the vehicle.