Pose Estimation Accuracy Improvement Using Different Orientation Representations With Neural Networks: Case Study for the VIVE HTC Tracker

Abstract

In robotic applications, the HTC VIVE tracker is frequently used for Learning from Demonstration. This solution and similar devices are not industrial-grade, meaning that their accuracy in tracking movements in three-dimensional space needs to be refined to ensure it is sufficient for programming precise robot positioning tasks. Several different methods are often used to improve position tracking accuracy, such as polynomial correction and neural networks. Various methods of parameterizing position and orientation are also frequently used, such as Euler angles or quaternions, although trade-offs between compactness and numerical stability mean that they are suitable for different correction methods. In this study, we explore four different ways of representing the pose orientation (axis-angle representation, quaternion, rotation matrix, Zhou representation) for the purpose of implementing pose tracking accuracy correction for the HTC VIVE tracker. The paper investigates the applicability of these representations for the Neural Network correction methods and compares the results with the classical polynomial correction method. As part of the study, three experiments were conducted involving measurements of the actual and measured positions of the robot and the tracker. An industrial UR5e robot arm from Universal Robots was used as the reference system for collecting measurement data, with an HTC VIVE tracker mounted on its wrist. The obtained results confirm that both the representation and neural network architecture significantly influence calibration effectiveness of HTC VIVE tracker. The results of the experiments showed that the Zhou parametrization of the orientation, combined with neural network rectification, performs best and results in a 17-fold improvement in the pose estimation accuracy of the HTC VIVE tracking system. The PMC algorithm offers a valuable alternative when fast calibration is required, providing significant accuracy improvements with minimal computational cost.