Generating Distinctive Marker Configurations for Robot Detection in Motion Capture Systems
Mahmoud Hamandi, Abdullah Mohamed Ali, Nikolaos Evangeliou, Anthony P. Tzes, Farshad Khorrami · 2025
Motion Capture (MoCap) systems are commonly used to track indoor robots, providing measurements for robot control, ground truth data for testing new algorithms, and training datasets for learning-based methods. However, placing markers for MoCap tracking typically requires users to select distinct configurations for each robot while ensuring marker visibility and avoiding symmetric arrangements. This paper presents an optimization-based method for generating distinct marker configurations that ensure visibility, non-symmetry, and spatial distribution. The proposed approach automates the marker placement process, supporting both geometrically defined shapes and CAD model-based robot surfaces. The implementation is released as an open-source, user-friendly software tool, enabling streamlined marker placement for robotics applications.