Monocular Odometry In Country Roads Based On Phase-Derived Optical Flow And 4-Dof Ego-Motion Model

Purpose - Positioning is a key task in most field robotics applications but can be very challenging in GPS-denied or high-slip environments. The purpose of this paper is to describe a visual odometry strategy using only one camera in country roads.Design/methodology/approach - This monocular odometery system uses as input only those images provided by a single camera mounted on the roof of the vehicle and the framework is composed of three main parts: image motion estimation, ego-motion computation and visual odometry. The image motion is estimated based on a hyper-complex wavelet phase-derived optical flow field. The ego-motion of the vehicle is computed by a blocked RANdom SAmple Consensus algorithm and a maximum likelihood estimator based on a 4-degrees of freedom motion model. These as instantaneous ego-motion measurements are used to update the vehicle trajectory according to a dead-reckoning model and unscented Kalman filter.Findings - The authors' proposed framework and algorithms are validated on videos from a real automotive platform. Furthermore, the recovered trajectory is superimposed onto a digital map, and the localization results from this method are compared to the ground truth measured with a GPS/INS joint system. These experimental results indicate that the framework and the algorithms are effective.Originality/value - The effective framework and algorithms for visual odometry using only one camera in country roads are introduced in this paper.