Real-Time Visual Tracking and Identification for a Team of Homogeneous Humanoid Robots
This addresses the challenge of enabling effective collaboration in multi-robot systems, particularly for humanoid robots, though it is incremental as it builds on existing methods like HOG and Hungarian algorithm.
The paper tackles the problem of mutual identification and tracking for a team of homogeneous humanoid robots collaborating on tasks, presenting a real-time vision-based system that uses Histogram of Oriented Gradients for detection and the Hungarian algorithm for optimal tracking, tested with two igus Humanoid Open Platform robots on a soccer field.
The use of a team of humanoid robots to collaborate in completing a task is an increasingly important field of research. One of the challenges in achieving collaboration, is mutual identification and tracking of the robots. This work presents a real-time vision-based approach to the detection and tracking of robots of known appearance, based on the images captured by a stationary robot. A Histogram of Oriented Gradients descriptor is used to detect the robots and the robot headings are estimated by a multiclass classifier. The tracked robots report their own heading estimate from magnetometer readings. For tracking, a cost function based on position and heading is applied to each of the tracklets, and a globally optimal labeling of the detected robots is found using the Hungarian algorithm. The complete identification and tracking system was tested using two igus Humanoid Open Platform robots on a soccer field. We expect that a similar system can be used with other humanoid robots, such as Nao and DARwIn-OP