Real-Time and Robust Stereo Visual Navigation Localization Algorithm Based on ORB

Abstract

The biggest challenge of visual navigation localization is feature extraction and association. Currently, the most widely used method is simple corner feature and simple matching strategy based on SAD or NCC. Another option is scale invariant feature and rotation invariant descriptor, typically as SIFT, SURF. Feature extraction and matching methods based on the SIFT or SURF are accurate and robust. However, its computational complexity is too high and not suitable for the real-time navigation localization task. This paper presents a new fast, accurate, robust stereo vision navigation localization method, based on a new developed ORB feature and descriptor. First, we presented our matching method based on ORB. Then, we obtained matching inliers and an initial motion estimation parameters using RANSAC and three points motion estimation method. Finally, nonlinear motion refinement method was used to polish the solution. Experimental results show that our method is robust, accurate and real-time.