FastQR: Fast Pose Estimation of Objects Based on Multiple QR Codes and Monocular Vision in Mobile Embedded Devices

In recent years, the pose estimation of objects has become a research hotspot. This technique can effectively estimate the pose changes of objects in space and is widely used in many mobile devices, such as AR/VR. At present, mainstream technologies can achieve high-precision pose estimation, but the problem of that of multiple irregular objects in mobile and embedded devices under limited resource conditions is still challenging. In this paper, we propose a FastQR algorithm that can estimate the pose of multiple irregular objects on Renesas by utilizing homography method to solve the transformation matrix of a single QR code and then establish the spatial constraint relationship between multiple QR codes to estimate the posture of irregular objects. Our algorithm obtained a competitive result in simulation and verification on the RZ/A2M development board of Renesas. Moreover, the verification results show that our method can estimate the spatial pose of the multiobject accurately and robustly in distributed embedded devices. The average frame rate calculated on the RZ/A2M can reach 28 fps, which is at least 37 times faster than that of other pose estimation methods.