Fast and Low-Drift Visual Odometry With Improved RANSAC-Based Outlier Removal Scheme for Intelligent Vehicles

Visual odometry estimates the ego-motion of a vehicle using only the input of a single or multiple cameras mounted on the vehicle. This paper focuses on the research of the stereo visual odometry system of intelligent vehicles, and discusses how to improve the robustness, accuracy and efficiency. A new robust estimation algorithm, Locally Optimized Progressive Sample Consensus algorithm, is proposed. Compared with the RANSAC algorithm, it can not only improve the accuracy of model estimation, but also can find more inliers to terminate the iteration process in advance, thereby speeding up the algorithm. A decoupling-based motion estimation algorithm is proposed. Monocular method is used to estimate the rotation parameters, which eliminates the influence of mismatching between left and right frames on rotation estimation. Moreover, when estimating the translational motion, the decoupling method makes the normalized re-projection error criterion better distinguish between inliers and outliers. The performance of the method is evaluated on the KITTI benchmark dataset by comparing it with the existing visual odometry systems. The experimental results show that the proposed technique has a high accuracy and efficiency.