Adaptive ORB feature detection with a variable extraction radius in RoI for complex illumination scenes

Feature detection is a crucial technique for a vision navigation system to estimate robot pose according to natural landmarks. It is difficult for the existing feature detection techniques to balance the feature quality, processing time and robustness for a vision-based robot in complex workspaces. An adaptive Oriented fast and Rotated Brief (ORB) feature detection method with a variable extraction radius in Region of Interest (RoI) is proposed to deal with these problems. Firstly, the original camera image is processed by means of the Laplace transform of Gaussian (LTOG) pyramid and the grayscale centroid method, in order to obtain the rotation and scale invariance for ORB features. Then, a RoI segmenting technique is developed to locate the image areas that contain potential ORB features due to obvious grayscale variation. Thirdly, the ORB features are extracted in RoIs by using a set of variable-radius templates, adaptive to different illumination conditions. Finally, a number of feature detection and robot localization experiments are conducted on a vision-based robot prototype in different scenes under complex illumination. The experimental results verify that the RoI segmenting technique can correctly preserve the grayscale-varying regions to search ORB features with scattered distribution but excluding the irrelevant areas to suppress feature noises, while the variable-radius template extraction method can detect more feature inliers in complex workspaces. Therefore, our adaptive ORB method can outperform other commonly-used algorithms in accuracy, efficiency and robustness.