Simplified epipolar geometry for real-time monocular visual odometry on roads

Simplified epipolar geometry is proposed in this paper to accelerate monocular visual odometry for ground vehicles. The vehicles on roads or indoors exhibit planar motion locally, and such prior has been effectively utilized to speed up monocular visual odometry. However, we observed that the previous planar motion models frequently failed because their over-simplification did not accept small non-planar motion caused by abrupt bumps or camera vibration. In this paper, simplified motion models are relaxed and their corresponding algorithms for relative pose estimation are derived. Effectiveness of the proposed algorithms is demonstrated by two types of experiments: relative pose estimation with synthetic data, and monocular visual odometry with real image sequences. In the first experiment, the proposed approximated 5-point algorithm provided similar (sometimes better) accuracy to the original 5-point algorithm, but it spent almost 15 times less computing time. In the second experiment, we also observed that monocular visual odometry with our algorithm had almost 9 times faster outlier rejection than previous approaches.