Generating Distinctive Marker Configurations for Robot Detection in Motion Capture Systems

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.

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Publication Details

Published
2025-02-12
DOI
https://doi.org/10.1109/icara64554.2025.10977589
Citations
4
Primary Topic
Human Pose and Action Recognition
Type
article
Field-Weighted Citation Impact
5.10
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article

Generating Distinctive Marker Configurations for Robot Detection in Motion Capture Systems

Mahmoud Hamandi, Anthony Tzes, Farshad Khorrami, Nikolaos Evangeliou et al.
4 citations
Human Pose and Action Recognition
5.10
article

Generating Distinctive Marker Configurations for Robot Detection in Motion Capture Systems

Mahmoud Hamandi, Anthony Tzes, Farshad Khorrami, Nikolaos Evangeliou, Abdullah Mohamed Ali
article en
4 citations

Abstract

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.

New York University (US)
Openalex Percentile: Top 5%
Human Pose and Action Recognition
5.10
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Generating Distinctive Marker Configurations for Robot Detection in Motion Capture Systems — Mahmoud Hamandi, Anthony Tzes, et al. · (2025) | TGRS Research Map | TGRS