UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers. (May 2020) - Muñoz-Salinas, Rafael; Medina-Carnicer, R.
Highlights: Novel SLAM method that fuses keypoints with squared fiducial markers. The proposed method can either use only markers, only keypoints, or a combination of them. Markers help to alleviate the relocalization problem since they remove the visual ambiguity in repetitive environments, and to obtain the map scale. Markers can be placed in strategic locations to help relocalization and reduce drift. Proposed method outperforms ORBSLAM2 and LDSO in public dataset KITTI, Euroc-MAV and TUM. Abstract: Simultaneous Localization and Mapping is the process of simultaneously creating a map of the environment while navigating in it. Most of the SLAM approaches use natural features (e.g. keypoints) that are unstable over time, repetitive in many cases or their number insufficient for a robust tracking (e.g. in indoor buildings). Other researchers, on the other hand, have proposed the use of artificial landmarks, such as squared fiducial markers, placed in the environment to help tracking and relocalization. This paper proposes a novel SLAM approach by fusing natural and artificial landmarks in order to achieve long-term robust tracking in many scenarios. Our method has been compared to the start-of-the-art methods ORB-SLAM2 [1], LDSO [2] and SPM-SLAM [3] in the public datasets Kitti [4], Euroc-MAV [5], TUM [6] and SPM [3], obtaining better precision, robustness and speed. Our tests also show that the combination of markers and keypoints achieves better accuracy than each one of them independently.