计算机科学
人工智能
计算机视觉
同时定位和映射
代表(政治)
机器人
无人机
智能摄像头
基础(拓扑)
国家(计算机科学)
可重用性
计算机图形学(图像)
移动机器人
软件
数学
数学分析
算法
政治
生物
政治学
法学
遗传学
程序设计语言
作者
Francisco J. Romero-Ramírez,Rafael Muñoz‐Salinas,Manuel J. Marín‐Jiménez,A. Carmona-Poyato,R. Medina-Carnicer
出处
期刊:Neurocomputing
[Elsevier]
日期:2024-01-01
卷期号:563: 126940-126940
被引量:3
标识
DOI:10.1016/j.neucom.2023.126940
摘要
State-of-the-art SLAM methods are designed to work only with the type of camera employed to create the map, and little attention has been paid to the reusability of the maps created. In other words, the maps generated by current methods can only be reused with the same camera employed to create them. This paper presents a novel SLAM approach that allows maps generated with one camera to be used by other cameras with different resolutions and optics. Our system allows, for instance, creating highly detailed maps processed off-line with high-end computers, to be reused later by low-powered devices (e.g. a drone or robot) using a different camera. The first map, called base map, can be reused with other cameras and dynamically adapted by creating an augmented map. The principal idea of our method is a bottom-up pyramidal representation of the images that allows us to match keypoints between different camera types seamlessly. The experiments conducted validate our proposal, showing that it outperforms the state-of-the-art approaches, namely ORBSLAM, OpenVSLAM and UcoSLAM.
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