Examinando por Autor "Romero-Ramirez, Francisco J"

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Large-Scale Indoor Camera Positioning Using Fiducial Markers
(MDPI, 2024-07) García-Ruiz, Pablo; Romero-Ramirez, Francisco J; Muñoz-Salinas, Rafael; Marín-Jiménez, Manuel J; Medina-Carnicer, Rafael
Estimating the pose of a large set of fixed indoor cameras is a requirement for certain applications in augmented reality, autonomous navigation, video surveillance, and logistics. However, accurately mapping the positions of these cameras remains an unsolved problem. While providing partial solutions, existing alternatives are limited by their dependence on distinct environmental features, the requirement for large overlapping camera views, and specific conditions. This paper introduces a novel approach to estimating the pose of a large set of cameras using a small subset of fiducial markers printed on regular pieces of paper. By placing the markers in areas visible to multiple cameras, we can obtain an initial estimation of the pair-wise spatial relationship between them. The markers can be moved throughout the environment to obtain the relationship between all cameras, thus creating a graph connecting all cameras. In the final step, our method performs a full optimization, minimizing the reprojection errors of the observed markers and enforcing physical constraints, such as camera and marker coplanarity and control points. We validated our approach using novel artificial and real datasets with varying levels of complexity. Our experiments demonstrated superior performance over existing state-of-the-art techniques and increased effectiveness in real-world applications. Accompanying this paper, we provide the research community with access to our code, tutorials, and an application framework to support the deployment of our methodology.
ReSLAM: Reusable SLAM with heterogeneous cameras
(Elsevier, 2024-01) Romero-Ramirez, Francisco J; Muñoz-Salinas, Rafael; Marín-Jiménez, Manuel J; Carmona-Poyato, Angel; Medina-Carnicer, Rafael
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.