CREAK descriptor evaluation for monocular visual SLAM

Abstract

This dissertation evaluates the novel Colour-based Retina Keypoint (CREAK) feature descriptor with the current state of the art, Fast Retina Keypoint (FREAK) feature descriptor in the context of a visual Simultaneous Localization and Mapping (SLAM) implementation. SLAM implementations are often the solution to autonomous navigation applications because of its profound error corrective capabilities, and therefore the FREAK and CREAK descriptors were evaluated in the context of SLAM. The concept of SLAM can be described as the method in which a robot builds a map of its surrounding environment, whilst simultaneously tracking its own movement through the map. Although the SLAM problem is considered solved on a conceptual level, there is always room for improvement, and the simplest place to look for improvement is in the initial phases of the SLAM algorithm, which provides the information that SLAM will use to estimate the map and robot’s position. One such phase is the descriptor algorithm used. In the specific case of visual SLAM, the vSLAM implementation is dependent on an estimation of the robot’s current pose, the location of the observed landmarks in the map, and the location of the robot in relation to the landmarks. This dissertation explores the algorithm that defines the appearance of each landmark, such that they can be recognized and robustly identified. Such a feature defining algorithm is known as a feature descriptor. Two variations of feature descriptors are discussed, namely FREAK – which is more well known in computer vision with a reputation of being robust and efficient, and CREAK – which has been proposed more recently, and although similar to FREAK, boasts superior robustness due to its novel ability to consider colour information in its description. The descriptors are used in a monocular Visual Odometry (VO) setting, and the trajectory determined on the KITTI Vision Benchmark Suite dataset. Results are obtained, documented and discussed. Finally, SLAM is implemented with the Extended Kalman Filter (EKF) where matched features along with their estimated map coordinates are used as the observed landmarks. It is shown that the CREAK descriptor is not necessarily a better descriptor compared to FREAK when implementing EKF-SLAM, however, the most significant finding is that of computational times. Despite FREAK being slightly faster than CREAK for monocular VO alone, CREAK is significantly faster than FREAK for the EKF-SLAM implementation presented, due to the nature of CREAK generating fewer – but equally accurate – matches per frame.