Multi-Stage Refinement Feature Matching Using Adaptive ORB Features for Robotic Vision Navigation

Abstract

Feature matching is a crucial technique to estimate robot pose in the light of natural landmarks for a vision navigation system. It is difficult for the present feature matching approaches to balance the accuracy, robustness and efficiency for a vision-based robot in complex workspaces. A multi-stage KNN-TF-BF-EN-RANSAC (KTBER) refinement matching technique is proposed to combine with the adaptive ORB feature extraction method, in order to develop a novel KTBER_AORB model that can outperform the state-of-the-art feature extraction and matching techniques. Firstly, the adaptive ORB (AORB) features are extracted in the Laplacian Transformation of Gaussian (LTOG) pyramid by using a set of variable-radius templates. The template radius is adjusted in terms of different brightness and contrast conditions. Then, the Eigenvector Norm (EN) and Bidirectional Filtering (BF) techniques are devised to establish a novel KTBER model, which can match AORB feature pairs not only according to the local appearance-based similarity of feature descriptors in the Hamming space, but also by means of the similarity of local image structure and global grayscale information for feature pairs in the Euclidean space. Finally, a large number of feature matching experiments are conducted on five public datasets and our mobile robot dataset, in order to test the algorithm performances for different challenging scenarios, including illumination change, motion blur, image rotation, viewpoint change, and low texture. On the one hand, an intensive experimental analysis of our model with different configurations is made to check the specific effects of different algorithm elements. On the other hand, the KTBER_AORB model is further compared with four state-of-the-art techniques, e.g., SIFT, ORB-SLAM, MP-ORB, and LAAT. The experimental results demonstrate that the KTBER_AORB model outperforms other state-of-the-art feature matching techniques on accuracy and robustness, still preserving a higher efficiency than most counterparts.