# Sparse Indoor Camera Positioning with Fiducial Markers
The absence of overlapping among the camera views complicates camera positioning in this environment. highlighting the urgent need for innovative solutions to tackle pose estimation in non-overlapping camera configurations effectively. This paper introduces several contributions, starting with a novel approach to address these challenges by introducing a method capable of accurately positioning any fixed camera set, regardless of whether they have overlapping views, in large indoor scenarios, as represented in Figure 2. Our method combines advanced optimization techniques with strategic marker placement facilitated by an auxiliary mobile camera to estimate the pose of any fixed camera accurately. To our knowledge, this is the first work to tackle the problem of fixed camera pose estimation without overlap in large and complex conditions. Additionally, we introduce an algorithm to automatically detect the set of markers that remain static between recordings made with a mobile camera, facilitating the usage of our method under any circumstances. Our last contribution is the presentation of a novel set of datasets specifically designed to test the capability of our method to accurately position fixed cameras without overlap, arranged in various configurations and scenarios. Our experiments validate our method as the first to achieve state-of-the-art results in positioning any fixed camera arrangement with and without overlapping fields of view. This work builds upon and significantly extends our previous methodology [5] by addressing its key limitations. Our previous approach requires cameras to have an overlapping field of view, thus forcing a very large number of cameras to solve the problem. In contrast, the method presented in this paper does not require any camera overlap, thus allowing its use in realistic scenarios. It is a more challenging problem addressed through strategically placed reusable fiducial markers and an auxiliary mobile camera that captures marker observations. The main contributions of this paper are as follows: (i) a novel approach is devised to estimate the pose of sparse cameras (without overlapping field of view) by combining mobile cameras and fixed views; (ii) a method is created to automatically determine the markers that remain static between recordings made with the mobile camera; (iii) a meta-graph is introduced to fuse all visual information into a single optimization process to reduce the reprojection error and other structural restrictions; (iv) datasets are created to evaluate the proposal. The rest of this work is organized as follows. Section 2 describes the related works that frame the context of our research. Section 3 describes our methodology in detail. Section 4  
Figure 2: **Overview of the proposed method. (a) First step: Some markers are placed and recorded using the mobile camera with frames \(\{f_{2},f_{3},f_{4},f_{5}\}\). Additionally, frame \(f_{1}\) is taken from the left fixed camera. Subset A consists of markers \(\{m_{1},m_{2},m_{5},m_{6}\}\) while Subset B consists of markers \(\{m_{3},m_{4},m_{7},m_{8}\}\). (b) Second step: Markers from Subset B are moved while markers from Subset A remain still, and the new marker arrangement is recorded again using the mobile camera with frames \(\{f_{6},f_{7},f_{8},f_{9},f_{10}\}\). (c) Third step: Markers from Subset A are moved, leaving the markers from Subset B static. The arrangement is recorded with the mobile camera with frames \(\{f_{11},f_{12},f_{13}\}\), and finally, frame \(f_{14}\) is taken with the right fixed camera. **  
presents experimental results validating the effectiveness of our approach. Section 5 discusses our findings' implications and outlines potential future research directions.