This paper proposes a novel approach for Simultaneous Localization and Mapping by fusing natural and artificial landmarks. Most of the SLAM approaches use natural landmarks (such as keypoints). However, they are unstable over time, repetitive in many cases or insufficient for a robust tracking (e.g. in indoor buildings). On the other hand, other approaches have employed artificial landmarks (such as squared fiducial markers) placed in the environment to help tracking and relocalization. We propose a method that integrates both approaches 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 and LDSO in the public dataset Kitti, Euroc-MAV, TUM and SPM, 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.

中文翻译：

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UcoSLAM：通过融合关键点和平方平面标记的同时定位和映射

本文提出了一种通过融合自然和人工地标进行同步定位和制图的新方法。大多数 SLAM 方法使用自然地标（例如关键点）。然而，它们随着时间的推移是不稳定的，在许多情况下是重复的，或者不足以进行稳健的跟踪（例如在室内建筑物中）。另一方面，其他方法采用放置在环境中的人工地标（例如平方基准标记）来帮助跟踪和重新定位。我们提出了一种集成两种方法的方法，以便在许多场景中实现长期稳健的跟踪。我们的方法与公共数据集 Kitti、Euroc-MAV、TUM 和 SPM 中的最先进方法 ORB-SLAM2 和 LDSO 进行了比较，获得了更好的精度、鲁棒性和速度。