A Robust 6-D Pose Tracking Approach by Fusing a Multi-Camera Tracking Device and an AHRS Module

Abstract

Optical tracking technique based on multiple cameras is widely employed in robots, virtual reality, and industrial measurements due to its excellent position accuracy. However, its 6-dimensional (6D) tracking practicability is impaired by the line-of-sight issue and the complicated tracker based on multiple markers. Hence, we implemented a custom-built hybrid tracking system consisting of four-camera equipment and a 6D tracker comprising a single optical marker and an automatic heading reference system (AHRS). AHRS provides the 3D orientation of the tracked object directly and compensates for the 3D position when the optical tracking is occluded. A two-stage cascaded adaptive Unscented Kalman Filtering (CAUKF) was proposed to enhance real-time fusion tracking performance. The CAUKF not only provides a reliable frequency enhancement solution for the optical tracking adapting to various frequencies, but also improves the continuality of prediction on fusion state variables and the corresponding covariance matrix, which helps to initialize the occlusion tracking accurately. When an occlusion occurs, a learning-based adaptive Unscented Kalman Filter (LAUKF) module can adaptively adjust noise estimation matrices in the unscented transformation according to the AHRS data, thereby significantly reducing the position estimation error. Experimental results reveal that the proposed tracking approach achieved 6D tracking at 100 Hz with 0.32 mm position error (mean absolute error) and 0.1° orientation error. This study furnishes a novel implementation method for the full 6D pose tracking with a simplified tracker structure and improved accuracy, continuity, and stability.