## 1 Introduction  
Intelligent manufacturing is a vital element of Industry 4.0, driving the advancement of autonomous mobile robot (AMR) technology [1, 2]. AMRs working in factories must traverse multiple workstations or machines, position with satisfactory precision, and perform various tasks [3, 4]. However, manufacturing environments are inherently dynamic due to human-robot coexistence [5, 6]. To achieve successful automation tasks, AMRs require accurate and reliable positioning systems.  
Despite extensive research on AMRs in recent years, achieving millimeter positioning accuracy in the absence of environmental modification remains a challenge. One solution to this challenge is to divide an AMR navigation mission into a pure navigating phase and a high-precision docking phase [5, 7]. To address the docking challenge, research has focusedon various target docking methods, including vision-based, laser-based, optics-based, ultrasonic-based, and other approaches.  
However, these approaches face challenges related to distinguishability and maintenance costs. Distinguishability refers to the target feature being easily confused with the background, leading to failed docking processes. This problem is often encountered in laser-based, optic-based, and ultrasonic-based methods. The vision-based methods used in docking missions primarily rely on fiducial markers installed on the ground or the surface of the target. However, current fiducial markers like ArUco or AprilTag are not compatible with commonly used information codes such as Quick Response (QR) codes and DataMatrix (DM). As a result, deploying these markers incurs significant costs as each target requires the installation of at least two markers and the establishment of a mapping relationship between the fiducial markers and information codes. Additionally, the performance of existing vision-based methods is sensitive to ambient light, leading to instability in the docking process under light-on and light-off conditions.  
To address these challenges, we have developed a fiducial marker reader system named FiMa-Reader, specifically designed for docking tasks. The FiMa-Reader system consists of both software and hardware modules. The hardware module comprises an embedded system, a near-infrared camera, and multiple 940 nm fill-lights. On the other hand, the software module comprises the detection algorithm of a novel two-dimensional marker pattern called the DataMatrix-ArUco-Hybrid (DAH) pattern, as well as an accelerated framework for the DAH pattern detection. The main contribution of the proposed technology are as follows:  
1. The proposed DAH pattern combines the ArUco and Data Matrix to increase information encoding capacity while maintaining simple features. The redundancy in the DAH pattern design allows it to function even in the presence of occlusion. Additionally, the composite design minimizes deployment costs as only one marker needs to be installed;
2. The proposed multithread acceleration framework distributes the detection tasks across multiple cores, resulting in a higher frame rate per second. This optimization maximizes the utilization of onboard resources and enables robots to dock with higher precision;
3. The FiMa-Reader system utilizes a near-infrared camera for detection, ensuring that ambient light does not affect the image quality. This ensures stable detection results under different indoor lighting conditions.  
The remainder of this paper is organized as follows. Section 2 reviews the related work in mobile robot docking areas in recent years. Section 3 introduces the software and hardware modules in the FiMa-Reader system. Section 4 presents four experiments and analyzes the results. Finally, a conclusion is given in Section 5.