## 5 Conclusions  
In this paper, we proposed a fiducial marker reader system called the FiMa-Reader system, which consists of a novel marker pattern, the DAH pattern, a low-cost embedded system, and MVI computer software version 1.0. The proposed DAH pattern combines two Data Matrix and two ArUco markers to form a new pattern that has a larger data capacity and more robust detection for occlusion situations. The embedded system uses an infrared camera and 940 nm fill-lights to overcome the influence of ambient light.  
This paper also proposes a framework for DAH pattern detection. To improve the DAH pattern observation rate for a smoother and more precise docking process, a multi-thread framework used for acceleration was proposed. The framework uses a thread pool to execute the preprocessing of each frame in parallel. The experimental results show that the DAH pattern outperforms a single ArUco or a single Data Matrix pattern in terms of detection rate and detection time. Using the FiMa-Reader system for the docking process can significantly improve the positioning accuracy compared to pure navigation. Further  
Figure 9: The positioning accuracy of the light-on and light-off scenarios. (**a**) The x-y positioning accuracy distribution, unit mm. (**b**) The theta position accuracy distribution, unit degrees. The blue dots represent the mean value, while the black squares indicate the presence of outliers.  
more, the FiMa-Reader system is not affected by ambient light, which means it can work during the day or night and output consistent high-precision observations.  
The proposed system still has certain limitations. For instance, at long distances, the detection rate may decrease due to the diminutive size of each submarker within the DAH pattern. Additionally, the existing rules only support mutual mapping between DM codes and ArUco, resulting in less scalability. Lastly, our multithreaded framework assumes the input from a single camera source at any given time, limiting the system's capability to handle multiple cameras simultaneously. Our future work will focus on improving the detection rate of the DAH pattern at long distances and supporting the simultaneous processing of multiple cameras in the multithreaded framework.  
[PERSON] and [PERSON] developed the original concept of this paper and designed the system framework. [PERSON] conducted the experiments and drafted the manuscript. [PERSON] and [PERSON] contributed to the data collection process. [PERSON] reviewed the manuscript and supervised the project. All authors have read and approved the final version of the manuscript.  
This work was funded by Key Research and Development Program of Shaanxi (Program No. 2021 ZDLGY09-03) and National Key Research and Development Plan (No. 2019 YFB17/07503 HZ-03).  
Not applicable.  
The code used in this study and datasets from Experiment 1 (DAH pattern performance) are available from the corresponding author upon reasonable request. The data are not publicly available due to the code and the datasets contain other commercially protected content unrelated to the methods mentioned in this paper, such as hardware drivers, software frameworks, and background and methods during data collection. This content can only be obtained for non-commercial purposes and after signing a confidentiality agreement with Shenzhen Youibot Robotics Co., Ltd. (Shenzhen, China).  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Shenzhen Youibot Robotics Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.