# Sparse Indoor Camera Positioning with Fiducial Markers
## 2 Related Works
However, the efficacy of this technique is heavily reliant on the quality of the initial SfM reconstruction. Further expanding on this concept, a novel method by [PERSON] et al. [33] integrates object detection into Gaussian splatting, potentially offering solutions to the challenges discussed in this paper. However, like other existing methods, without overlapping views, its application remains limited. Simultaneous localization and mapping (SLAM) is a principal method among feature-based techniques, enabling a camera to construct a map of an unknown environment while concurrently determining its location within that map. Existing methods, such as those proposed by [PERSON] et al. [16] and [PERSON] et al. [17], demonstrate significant effectiveness. However, despite their capabilities, SLAM methodologies encounter difficulties in non-overlapping camera networks, primarily due to the lack of shared visual features, which are essential for traditional SLAM operations. For these reasons, new methodologies based on SLAM, such as those proposed by [PERSON] et al. [6] and [PERSON] et al. [18], are being developed to tackle the challenges posed by non-overlapping environments. While these methods showcase innovative approaches to extend SLAM techniques beyond their traditional constraints, they inherently rely on environments rich in identifiable details. This reliance stems from their dependence on feature-based techniques, necessitating dense environmental data to function effectively. Despite the effectiveness of feature-based systems, marker-based systems provide a superior solution for accurate pose estimation in controlled scenarios. Utilizing fiducial markers like ArUco [34], these systems are robust and easy to detect, generally requiring markers to be visible in multiple camera views to establish correspondences. In non-overlapping scenarios, however, the effectiveness of marker-based systems is reduced, as the lack of shared marker observations prevents the direct computation of relative poses. To address this, methods such as the one proposed by [PERSON] et al. [19] offer solutions using an additional camera and a chessboard pattern. However, these solutions are constrained to smaller scales and are far removed from real-world applications. At a larger scale, MarkerMapper [4] offers a notable solution designed to perform simultaneous localization and mapping using fiducial markers. It capitalizes on the detection of markers to build a map of the environment and estimate the camera's pose within that map. While effective in environments where markers are visible across multiple views, its dependency on overlapping fields of view limits its utility in non-overlapping camera networks. Lastly, the method proposed by [PERSON] et al. [5] extends MarkerMapper specifically to navigate the complexities of large-scale scenarios with some overlapping views. Their strategy utilizes reusable markers alongside scene geometry constraints, significantly enhancing pose estimation accuracy in vast and intricate environments. The method is based on placing markers in the overlapping view of two fixed cameras to enable the computation of the inter-camera pose. However, it is not suited for scenarios where cameras operate without any overlapping fields, as the method relies on the presence of at least some shared markers for accurate positioning. This paper introduces a novel approach for estimating the pose of sparse (i.e., non-overlapping) fixed camera networks. Our approach fundamentally differs from existing methodologies by utilizing a reusable set of markers, which are iteratively mapped using an auxiliary camera that records every placed marker. We enhance this method by integrating advanced optimization techniques and leveraging scene geometry, aiming to achieve precise pose estimation without relying on overlapping fields of view. To our knowledge, this innovative approach addresses a challenge that existing methods have not effectively resolved.