Mobile MoCap: A New Way to Track Motion
Mobile MoCap combines cameras and retroreflectors for improved tracking.
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Table of Contents
Motion capture technology helps robots know where they are in relation to other objects. This is important for making robots work safely and effectively around people. There are many ways to track the movement of robots, but one new method combines low-cost cameras and special markers that reflect light.
What is Mobile MoCap?
Mobile MoCap is a system that uses Retroreflectors, which are special markers that bounce near-infrared light back to a camera. This allows robots and other moving objects to be tracked even when they're in motion. Unlike traditional motion capture systems that use fixed cameras, Mobile MoCap is portable and can easily move with the objects or robots being tracked.
How Does It Work?
The Mobile MoCap system uses two infrared cameras that can see the retroreflectors. When these markers are placed on an object, the cameras can detect where the object is located in three-dimensional space. This is called Pose Estimation, which means figuring out the position and orientation of the object.
The system works by shining light from infrared LEDs onto the retroreflectors. The markers reflect this light back to the cameras, which capture images even when the lighting is poor or in complete darkness. The retroreflectors can be arranged in different shapes, which helps the cameras identify them quickly.
Comparison to Existing Systems
Many robots used in industry and research use Fiducial Markers, such as AprilTags. These are printed shapes that cameras can track to understand where the robot is. However, they have limitations and can struggle with accuracy, especially when the camera is moving fast or when there's a lot of motion blur.
Mobile MoCap outperforms systems that rely on fiducial markers. By using retroreflectors, it can provide more accurate position data even under challenging conditions. The system is built to adapt to various distances, angles, and speeds.
Testing the System
To see how well Mobile MoCap works, a series of tests were conducted. The system was placed on a moving platform where it could track a target with both retroreflectors and AprilTags. The results showed that Mobile MoCap consistently provided better Tracking Accuracy than the fiducial markers.
The tests included scenarios where the cameras were static and moving, with different speeds and angles. The Mobile MoCap system kept track of the target with impressive precision, even as it moved.
Advantages of Mobile MoCap
Cost-Effective: The system is built using affordable off-the-shelf components. This makes it accessible for many robotics applications without needing high budgets.
Portability: Unlike traditional setups that require fixed cameras, Mobile MoCap can move with the objects it tracks. This gives it a lot of flexibility.
High Accuracy: Thanks to its unique design and the use of retroreflectors, the system achieves higher localization accuracy compared to traditional methods.
Reduced Motion Blur: By minimizing the exposure time of the cameras, Mobile MoCap is less affected by motion blur. This means it can still track objects accurately even when they’re moving quickly.
Challenges Faced
While Mobile MoCap shows great promise, there are some challenges. Unlike fiducial markers, the retroreflectors don’t have built-in identification. This means that while the system can find the markers, it lacks a way to tell different markers apart without additional work.
Additionally, the system can face issues with reflective surfaces. For instance, shiny objects may confuse the cameras and lead to false readings. This can happen in environments with bright sunlight or reflective materials nearby.
Future Developments
There are plans to improve the Mobile MoCap system. Future iterations will aim to use cameras that can work in different light ranges, providing even more flexibility for indoor and outdoor use. Moreover, researchers intend to explore more complex tracking scenarios where multiple robots and targets need to be tracked simultaneously.
These advancements could enable the system to work in various applications, from industrial automation to virtual reality, where tracking moving objects is essential.
Applications of Mobile MoCap
Mobile MoCap can be applied in many fields, including:
Robotics: It helps robots understand their surroundings better, making it safer for them to operate alongside humans.
Virtual Reality: In gaming and training, it can track the movement of players to create more immersive experiences.
Healthcare: It can be used to monitor patients' movements and enhance therapies.
Research: In experimental setups, the system can help track various objects and their interactions in real-time.
Conclusion
Mobile MoCap represents an exciting advancement in motion capture technology. By making use of affordable hardware and innovative techniques, it offers a practical solution for tracking objects in motion. While there are still challenges to face, the potential for this system is vast, and it opens the door for new applications and improvements in the field of robotics and beyond. The pursuit of effective and reliable motion capture continues, paving the way for smarter and safer interactions between robots and people.
Title: Mobile MoCap: Retroreflector Localization On-The-Go
Abstract: Motion capture through tracking retroreflectors obtains highly accurate pose estimation, which is frequently used in robotics. Unlike commercial motion capture systems, fiducial marker-based tracking methods, such as AprilTags, can perform relative localization without requiring a static camera setup. However, popular pose estimation methods based on fiducial markers have lower localization accuracy than commercial motion capture systems. We propose Mobile MoCap, a system that utilizes inexpensive near-infrared cameras for accurate relative localization even while in motion. We present a retroreflector feature detector that performs 6-DoF (six degrees-of-freedom) tracking and operates with minimal camera exposure times to reduce motion blur. To evaluate the proposed localization technique while in motion, we mount our Mobile MoCap system, as well as an RGB camera to benchmark against fiducial markers, onto a precision-controlled linear rail and servo. The fiducial marker approach employs AprilTags, which are pervasively used for localization in robotics. We evaluate the two systems at varying distances, marker viewing angles, and relative velocities. Across all experimental conditions, our stereo-based Mobile MoCap system obtains higher position and orientation accuracy than the fiducial approach. The code for Mobile MoCap is implemented in ROS 2 and made publicly available at https://github.com/RIVeR-Lab/mobile_mocap.
Authors: Gary Lvov, Mark Zolotas, Nathaniel Hanson, Austin Allison, Xavier Hubbard, Michael Carvajal, Taskin Padir
Last Update: 2023-06-30 00:00:00
Language: English
Source URL: https://arxiv.org/abs/2303.13681
Source PDF: https://arxiv.org/pdf/2303.13681
Licence: https://creativecommons.org/licenses/by/4.0/
Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.
Thank you to arxiv for use of its open access interoperability.