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

## TL;DR

UcoSLAM combines natural keypoints and artificial squared markers to improve robustness and accuracy in SLAM, outperforming existing methods in various datasets by leveraging the complementary strengths of both landmark types.

## Contribution

The paper introduces a novel SLAM approach that fuses keypoints and squared planar markers for enhanced long-term robustness and accuracy in diverse environments.

## Key findings

- Outperforms ORB-SLAM2 and LDSO in precision and robustness
- Achieves better accuracy by combining markers and keypoints
- Demonstrates improved speed and reliability across multiple datasets

## Abstract

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.