## 1 Introduction  
Recent advancements in mobile robotics and autonomous driving technologies have established position and orientation (pose) estimation as a fundamental technology in contemporary robotic systems [1; 2]. The development of accurate and precise pose estimation algorithms is crucial for the effective operation of mobile robots [3; 4]. These technologies have found widespread applications across diverse environments, including public spaces, industrial facilities, and commercial settings [5; 6; 7; 8]. To accommodate the varying characteristics of environments where mobile robots and autonomous systems operate, researchers have developed multiple approaches to pose estimation, including the use of graphical markers, Wi-Fi positioning, Bluetooth technology, ultrasonic sensors, and light detection and ranging (LiDAR) systems. Additionally, simultaneous localization and mapping (SLAM) technology has emerged as a significant advancement in this field [9; 10; 11; 12; 13].  
Pose estimation algorithms using graphical markers as fiducial markers have gained considerable attention in mobile robot systems due to their straightforward implementation and cost-effectiveness [14; 15]. Prominent examples of graphical markers employed in mobile robot systems include ArUco, AprilTag, and ARTag [16; 17; 18]. In these systems, pose estimation is achieved by using cameras mounted on mobile robots to recognize graphical markers as fiducial markers and subsequently calculate the relative pose between the camera and the markers [19].  
While image-based pose estimation using graphical markers offers simplicity in implementation, its performance is significantly influenced by environmental factors. The environmental factors include ambient lighting conditions, spatial arrangement of objects in the environment, capture angles relative to the graphical markers, and the presence of physical obstacles. The environmental factors can substantially affect image quality and, consequently, the accuracy of pose estimation results.  
Several research studies have been conducted to enhance pose estimation performance using single graphical markers, including the implementation of Kalman filters for smoothing [20], the usage of adaptive filtering algorithms and quaternions [21], the utilization of multi-camera systems for enhanced precision [22; 23], the incorporation of additional features into graphical markers for improved accuracy [24], and the application of artificial intelligence techniques for image correction and pose estimation optimization [25; 26].  
Furthermore, researchers have investigated the use of multiple graphical markers to enhance pose estimation performance [27; 28]. These studies have demonstrated improvements through strategic placement of graphical markers in three-dimensional space and multi-angle recognition. However, these systems are still susceptible to performance degradation when measurement errors arise in specific graphical markers.  
This paper presents a novel algorithm designed to enhance the performance of pose estimation through the simultaneous capture and processing of multiple graphical markers using a single camera. The proposed algorithm needs to determine which graphical marker will serve as the coordinate system origin. The proposed algorithm incorporates an outlier detection mechanism to identify and exclude graphical markers that produce irregular and different pose estimates. The algorithm enhances the performance of pose estimation by averaging estimates from multiple graphical markers. The algorithm has been developed with low computational complexity, allowing it to conduct pose estimation efficiently even in small mobile robots with limited computational resources.  
Our approach employs estimation from multiple graphical markers while preventing any single marker from dominating the estimation process. The algorithm demonstrates robust performance even when certain markers produce substantial estimation errors and become difficult to identify.