Investigating the Fiducial Marker Network Characteristics for Autonomous Mobile Indoor Robot Navigation Using ROS and Gazebo
Publication: Journal of Construction Engineering and Management
Volume 148, Issue 10
Abstract
Building service robots rely on dense instrumentation of the building (e.g., Bluetooth beacons) or require high computational capabilities [e.g., simultaneous localization and mapping (SLAM)]. To overcome these limitations, studies explored a landmark-based localization and navigation approach based on inexpensive, computationally efficient, and easily configurable fiducial markers (e.g., AprilTags). However, context-specific assumptions were made regarding the fiducial marker characteristics and sensor configurations. Taking this forward, this study develops a generalized simulation-based approach to determine the optimal design characteristics of the fiducial marker network to achieve successful autonomous mobile indoor robot navigation with low instrumentation and computation. Different marker and camera parameters such as marker size and marker placement to optimize the density of fiducial markers (i.e., the ideal distance between subsequent markers) were investigated using Robot Operating Systems (ROS) and Gazebo (simulator) software. The simulation experiments focused on a specific robotic platform (i.e., TurtleBot3) and marker type (i.e., AprilTag). Results from the simulations suggested that with an increase in marker distance, the navigation success rate does not necessarily decrease. In addition, a marker size of 0.1 m performed the best in terms of navigation success rate (as high as 100% for a specific combination of marker height, marker size, and marker-to-marker distance). Future work aims to test this in a real-world setting to compare and analyze the simulated and actual performance of the robot. The proposed methodology is generic and can be applied to any mobile robotic platform, marker type, building type (e.g., residential), and application (e.g., construction progress monitoring).
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential and may only be provided upon reasonable request with restrictions.
Acknowledgments
This research was partially carried out using the Core Technology Platforms (CTP) resources at New York University Abu Dhabi (NYUAD). The drift experiments with the TurtleBot were performed using the Motion Capture System - V16 Cameras - Vicon available at the Kinesis Core Technology Platform. The authors thank Nikolaos Giakoumidis and Min Kyu Jung for their assistance with the experiments. In addition, the authors would like to thank Yiming Huang for her involvement during the simulations.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 148 • Issue 10 • October 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 31, 2021
Accepted: May 25, 2022
Published online: Aug 12, 2022
Published in print: Oct 1, 2022
Discussion open until: Jan 12, 2023
ASCE Technical Topics:
- Automation and robotics
- Buildings
- Computer models
- Computer networks
- Computing in civil engineering
- Engineering fundamentals
- Equipment and machinery
- Geomatics
- Instrumentation
- Mapping
- Measurement (by type)
- Models (by type)
- Navigation (geomatic)
- Sensors and sensing
- Structural engineering
- Structures (by type)
- Surveying methods
- Systems engineering
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Cited by
- Siwei Chang, Ming-Fung Francis Siu, Heng Li, Development of a Fuzzy Logic Controller for Autonomous Navigation of Building Inspection Robots in Unknown Environments, Journal of Computing in Civil Engineering, 10.1061/JCCEE5.CPENG-5060, 37, 4, (2023).