Follow Pedro! An Infrared-based Person-Follower using Nonlinear Optimization
This work addresses the challenge of person-following for robotics applications, but it is incremental as it builds on existing methods like particle filters and nonlinear optimization without introducing major innovations.
The researchers tackled the problem of enabling a wheeled robot to follow a person by developing a complete system using ROS2, with perception components that detect AR markers or IR beacons and navigation based on sensors like fisheye cameras and lidar, resulting in a proof-of-concept demonstration of AI research within the ROS2 framework.
We used ROS2 as a platform to conduct AI research for developing a Follow-Me capability as a proof-of-concept on a wheeled robot, demonstrating that AI research is possible in the ROS2 framework. We developed a complete system that uses perception and navigation components based on a sensor suite of fisheye cameras, lidar, and IMU running on an ARM64 Embedded Linux platform that runs ROS2 natively. The perception package detects AR markers and/or IR beacons to track a person. The tracker uses AI algorithms such as particle filters and nonlinear optimization to extract the SE(3) information of the 2D feature.