Visual Odometry with Drift-Free Rotation Estimation Using Indoor Scene Regularities

Abstract

We propose a hybrid visual odometry algorithm to achieve accurate and low-drift state estimation by separately estimating the rotational and translational camera motion. Previous methods usually estimate the six degrees of freedom camera motion jointly without distinction between rotational and translational motion. However, inaccuracy in the rotation estimate is a main source of drift in visual odometry. We design a hybrid visual odometry algorithm which separately estimates the rotational and translational motion to achieve improved accuracy and low drift error. To improve the accuracy of rotational motion estimation, we exploit orthogonal planar structures, such as walls, floors, and ceilings, common in man-made environments. We track orthogonal frames with an efficient SO(3)-constrained mean-shift algorithm, resulting in drift-free rotation estimates. Based on the absolute camera orientation, we newly propose a way to compute the translational motion by minimizing the de-rotated reprojection error with the tracked features. We compare the proposed algorithm with other state-of-the-art visual odometry methods and demonstrate an improved performance and lower drift error.