Paper Info
Title
A lightweight and scalable visual-inertial motion capture system using fiducial markers
Abstract
Accurate localization of a moving object is important in many robotic tasks. Often an elaborate motion capture system is used to realize it. While high precision is guaranteed, such a complicated system is costly and limited to specified small size workspace. This paper describes a lightweight and scalable visual-inertial approach, which leverages paper printable, known size and unknown pose, artificial landmarks, as called fiducials, to obtain motion state estimates, including pose and velocity. Visual-inertial joint optimization using incremental smoother over factor graph and the IMU preintegration technique make our method efficient and accurate. No special hardware is required except a monocular camera and an IMU, making our system lightweight and easy to deploy. Using paper printable landmarks, as well as the efficient incremental inference algorithm, renders it nearly constant-time complexity and scalable to large-scale environment. We perform an extensive evaluation of our method on public datasets and real-world experiments. Results show our method achieves accurate state estimates and is scalable to large-scale environment and robust to fast motion and changing light condition. Besides, our method has the ability to recover from intermediate failure.
Year
DOI
Venue
2019
10.1007/s10514-019-09834-7
AUTONOMOUS ROBOTS
Keywords
Field
DocType
Motion capture system,Visual-inertial,Fiducial based,IMU preintegration,Incremental smoothing
Factor graph,Motion capture,Computer vision,Fiducial marker,Inference,Computer science,Workspace,Inertial measurement unit,Artificial intelligence,Inertial motion capture,Scalability
Journal
Volume
Issue
ISSN
43.0
7
0929-5593
Citations 
PageRank 
References 
0
0.34
17
Authors
3
Name
Order
Citations
PageRank
Guoping He100.34
Shangkun Zhong200.34
Jifeng Guo322.75