Sparse Indoor Camera Positioning with Fiducial Markers

Abstract

Accurately estimating the pose of large arrays of fixed indoor cameras presents a significant challenge in computer vision, especially since traditional methods predominantly rely on overlapping camera views. Existing approaches for positioning non-overlapping cameras are scarce and generally limited to simplistic scenarios dependent on specific environmental features, thereby leaving a significant gap in applications for large and complex settings. To bridge this gap, this paper introduces a novel methodology that effectively positions cameras with and without overlapping views in complex indoor scenarios. This approach leverages a subset of fiducial markers printed on regular paper, strategically placed and relocated across the environment and recorded by an additional mobile camera to progressively establish connections among all fixed cameras without necessitating overlapping views. Our method employs a comprehensive optimization process that minimizes the reprojection errors of observed markers while applying physical constraints such as camera and marker coplanarity and the use of a set of control points. To validate our approach, we have developed novel datasets specifically designed to assess the performance of our system in positioning cameras without overlapping fields of view. Demonstrating superior performance over existing techniques, our methodology establishes a new state-of-the-art for positioning cameras with and without overlapping views. This system not only expands the applicability of camera pose estimation technologies but also provides a practical solution for indoor settings without the need for overlapping views, supported by accessible resources, including code, datasets, and a tutorial to facilitate its deployment and adaptation.