A Study of Vicon System Positioning Performance
By Pierre Merriaux, Y. Dupuis, Rémi Boutteau, P. Vasseur, X. Savatier. Italian National Conference on Sensors, 2017
417 citations recorded by the source corpus.
Motion capture setups are used in numerous fields. Studies based on motion capture data can be found in biomechanical, sport or animal science. Clinical science studies include gait analysis as well as balance, posture and motor control. Robotic applications encompass object tracking. Today’s life applications includes entertainment or augmented reality. Still, few studies investigate the positioning performance of motion capture setups. In this paper, we study the positioning performance of one player in the optoelectronic motion capture based on markers: Vicon system. Our protocol includes evaluations of static and dynamic performances. Mean error as well as positioning variabilities are studied with calibrated ground truth setups that are not based on other motion capture modalities. We introduce a new setup that enables directly estimating the absolute positioning accuracy for dynamic experiments contrary to state-of-the art works that rely on inter-marker distances. The system performs well on static experiments with a mean absolute error of 0.15 mm and a variability lower than 0.025 mm. Our dynamic experiments were carried out at speeds found in real applications. Our work suggests that the system error is less than 2 mm. We also found that marker size and Vicon sampling rate must be carefully chosen with respect to the speed encountered in the application in order to reach optimal positioning performance that can go to 0.3 mm for our dynamic study.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/8394ebff056536acd8494aebd6613a586f176a5c
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Citations in the corpus
- Towards 3D human pose construction using wifi (2020, methodology): “Our VICON system can estimate the position of the markers with errors less than 2mm [28].”
- Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators (2019, methodology): “So it becomes difficult to identify the sources of modeling error or in developing optimal design strategies [30].”