Real-Time Visual Tracking and Identification for a Team of Homogeneous Humanoid Robots

TL;DR

This paper presents a real-time vision-based system for detecting and tracking homogeneous humanoid robots, enabling effective collaboration through mutual identification using image analysis, heading estimation, and optimal labeling algorithms.

Contribution

The work introduces a novel real-time vision-based approach combining GOG descriptors, heading estimation, and Hungarian algorithm for tracking homogeneous humanoid robots.

Findings

Successfully tested with two humanoid robots on a soccer field.

Achieved real-time detection and tracking of robots.

System adaptable to different humanoid platforms.

Abstract

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