Improved pose estimation by inlier refinement for visual odometry

Abstract

Visual odometry is a well-known technique that is used to compute the rotation and translation of any moving vehicle with the help of camera mounted over it. This task of vision-based navigation is used for different applications such as autonomous navigation, motion tracking and obstacle detection, etc. This paper illustrates an approach for estimating vehicle motion by detecting and matching scale invariant SURF feature over consequent image frames. These set of matched feature are passed through an outlier removal and inlier selection methodology sequentially in order to remove the inconsistent features. Additionally, the proposed scheme incorporates the bucketing technique to ensure spatial distribution of feature in the overall image space. The proposed scheme of inlier selection-cum-outlier rejection has been applied on the KITTI dataset available online and is found to work satisfactorily as compared to the individual outlier rejection or inlier selection mechanism.