In recent years, the use of low-cost vision sensors to achieve navigation and positioning has received more and more attention. As vision sensors have high measurement accuracy, wide range, rich information, and non-contact, flexible, portable, and low-cost characteristics, they can achieve large-scale multi-target tracking and complete positioning tasks in complex and limited industrial field environments. We study an indoor visual positioning system based on camera and QR code. Firstly, the effective recognition range of the QR code beacon is analyzed, and the calculation formula of recognition range based on marker size, camera definition, and other parameters is derived. Based on this formula, the layout of the QR code beacon in the positioning scene is designed, and the system positioning is realized by the perspective n points (PnP) calibration algorithm. Finally, the validity of the QR code recognition range is verified by experiments.

We conduct the following research based on the existing perspective four points (P4P) QR code location algorithm: 1) We define the recognition range of the QR code and deduce the recognition range calculation formula according to the recognition algorithm accuracy, QR code size, camera resolution, and camera field of view (FOV). 2) According to the definition and calculation of the recognition range of the QR code, we design the QR code beacon deployment scheme of the target scene, realize a large range of positioning and recognition range coverage with fewer QR codes, improve the recognition rate, and ensure the accuracy of the positioning algorithm. 3) We analyze the positioning effect of the system under the fixed and mobile states of the camera position, calculate the positioning accuracy and positioning recognition rate of the system under different conditions, and verify the theoretical recognition range and positioning recognition rate.

The actual test environment is a room of 7 m×5 m×3 m (Fig. 6), and the relevant experimental parameters are shown in Table 1. To ensure the overall accuracy of the positioning system and the success rate of positioning, the spacing of the QR code beacon is reduced during the actual deployment, and the spacing is set to 2 m. According to the situation of the room, the space rectangular coordinate system is established, and four positions are marked in Table 1 to deploy QR codes so that the identification range can cover the whole room. To verify the effectiveness of the identification range algorithm and deployment scheme, we design two experiments. Experiment 1: To verify the positioning accuracy of fixed positions within the recognition range, we carry out positioning accuracy tests at different positions within the recognition range of four QR codes. The test results are shown in Fig. 7. After testing, the error of the QR code located at the edge of the identification range is slightly larger than that of the center of the identification range. The positioning error near the right below the QR code is less than 6 cm, and the positioning error near the edge of the identification range is less than 10 cm. The overall average positioning error is 8.32 cm, which is basically consistent with the positioning error of the algorithm theory. The positioning accuracy within the recognition range of the QR code is not affected. Experiment 2: The recognition rate is tested in the positioning scene (Fig. 8). Raspberry PI 3B is utilized to build the robot platform, and the camera is deployed on the robot to make the robot move around the room at a constant speed of 0.33 m/s along a straight or circular route. During the process, the positioning data is collected at a constant time interval and the number of successful positioning is calculated. In the experiment, the positioning program counts the number of successful positioning. Whenever the program successfully identifies the QR code and outputs the positioning result, and the positioning position deviated from the actual position or route is no more than 15 cm, it is regarded as a successful positioning, and the deviation distance from the route is regarded as the positioning error. The test results (Table 2) show that when the robot moves along a straight line or a ring route, the recognition rates of the QR code are 92.31% and 91.59%, respectively. Within the recognition range of the QR code, the positioning recognition rate meets the requirements. At the same time, the cumulative distribution function curve of positioning error in the fixed position and the moving process is shown in Fig. 9. It can be seen from Fig. 9 that when the robot moves, the positioning error distribution curve of the system moves better in a straight line than that in a circular motion. In addition, the error of the two methods has little change compared with the average positioning error of the fixed position, and the error of 90% positioning results is less than 9 cm. It shows that the positioning accuracy is basically not affected when the robot moves within the QR code recognition range, and the QR code beacon deployment scheme designed in the experiment meets the requirements of positioning accuracy and positioning success rate.

We study the recognition range of indoor visual positioning system based on the QR code and the deployment scheme of QR code beacons. To improve the deployment efficiency of the QR code and the coverage range of the positioning system, we first define the recognition range of the QR code and derive the calculation formula of the positioning recognition range according to the performance of the QR code recognition algorithm, marker size, camera definition, and other parameters. Then, the deployment strategy of QR code beacons is given for the positioning scenario, and the validity of the QR code positioning recognition range and beacon deployment scheme is verified by experiments. The results show that in the recognition range of the QR code, the average positioning error of fixed position is 8.32 cm. In the positioning scenario of QR code beacons, the positioning system is deployed on the robot, and the system is in linear and circular motions. The recognition rates of the QR code are 92.31% and 91.59%, respectively, which meets the positioning coverage requirements, and the positioning accuracy is almost consistent with the average positioning error of fixed position. Our QR code beacon deployment strategy has a good effect verified by experimental tests and improves the positioning efficiency and system reliability of the QR code indoor positioning algorithm based on P4P.