An Improved Visual Odometer Based on Lucas-Kanade Optical Flow and ORB Feature

Abstract

Visual odometer is an important part of SLAM system. Visual odometer based on feature points is the current mainstream. Featured based method completes the positioning by extracting feature points and matching descriptors. However, under low texture conditions, there are few features to extract, and the cost of matching descriptors is also high. to solve this problem, we propose a visual odometer based on Lucas-Kanade(LK) optical flow method and ORB feature. The method improves the performance of the ORB algorithm and acquires more feature points in a low-texture environment. A coarse-to-fine LK method is proposed for robustly and efficiently matching feature points between two frames. Based on this, a semi-direct visual odometry is proposed to improve the performance of visual odometry in low-texture environments. The main contribution of this method is that we introduce the direct method in the feature point-based approach, which significantly improves the localization accuracy in low-texture environments. The results show that the method can accomplish localization in low-texture environments without closed-loop. Experimental evaluation on two benchmark datasets shows that the method has higher accuracy and robustness in motion estimation compared to other state-of-the-art methods. We have done strict tests on EuRoC and TUM data sets, and the results show that our visual odometer has higher accuracy and robustness in low-texture environment than the state-of-art feature-based scheme.

Keywords

• LK optimal flow
• motion estimation
• CLAHE
• information entropy
• ORB