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This paper proposes a novel approach for Simultaneous Localization andMapping by fusing natural and artificial landmarks. Most of the SLAM approachesuse natural landmarks (such as keypoints). However, they are unstable overtime, repetitive in many cases or insufficient for a robust tracking (e.g. inindoor buildings). On the other hand, other approaches have employed artificiallandmarks (such as squared fiducial markers) placed in the environment to helptracking and relocalization. We propose a method that integrates bothapproaches 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 andLDSO in the public dataset Kitti, Euroc-MAV, TUM and SPM, obtaining betterprecision, robustness and speed. Our tests also show that the combination ofmarkers and keypoints achieves better accuracy than each one of themindependently.

UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markers