mdBRIEF - a fast online-adaptable, distorted binary descriptor for real-time applications using calibrated wide-angle or fisheye cameras

摘要

Fast binary descriptors build the core for many vision based applications with real-time demands like object detection, visual odometry or SLAM. Commonly it is assumed, that the acquired images and thus the patches extracted around keypoints originate from a perspective projection ignoring image distortion or completely different types of projections such as omnidirectional or fisheye. Usually the deviations from a perfect perspective projection are corrected by using standard undistortion models. The latter, however, introduce artifacts if the camera’s field-of-view gets larger. In addition, many applications (e.g. monocular SLAM) require only undistorted points and holistic undistortion of every image for descriptor extraction could be eluded. In this paper, we propose a distorted and masked version of the BRIEF descriptor for calibrated cameras, called dBRIEF and mdBRIEF respectively. Instead of correcting the distortion holistically, we distort the binary tests and thus adapt the descriptor to different image regions. The implementation of the proposed method along with evaluation scripts can be found online at https://github.com/urbste/mdBRIEF.