Smulders, DehanUren, KennethVan Schoor, GeorgeVan Daalen, CornéEngelbrecht, Japie2019-06-072019-06-072019Smulders, D. et al. 2019. CREAK descriptor evaluation for visual odometry. Proceedings, 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA), Bloemfontein, South Africa, 28-30 Jan. Article no 8704807:188-193. [https://doi.org/10.1109/RoboMech.2019.8704807]978-1-7281-0369-3 (Online)http://hdl.handle.net/10394/32604https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8704807https://doi.org/10.1109/RoboMech.2019.8704807Each year, latest state of the art technologies and algorithms arise that claim and prove to-shine their predecessors. One such algorithm is the Colour-based Retina Key-point (CREAK) descriptor, which is based on the FAST Retina Key-point (FREAK) descriptor with the included functionality of considering colour information in its Key-point description. This paper explores the implementation of CREAK in a “real-time” visual odometry application by means of a comparative study of the more well-known FREAK algorithm. Although FREAK achieved more accurate odometry when key-points were abundant, this proved to be too computationally expensive. CREAK on the other hand outperformed FREAK when key-points are scarce due to its lack of false-positive matchesenFREAKCREAKVisual odometryFeature descriptorEvaluationPresentation