A Visual Leader-Following Approach With a T-D-R Framework for Quadruped Robots

The quadruped robot imitates the motions of four-legged animals with a superior flexibility and adaptability to complex terrains, compared with the wheeled and tracked robots. Its leader-following ability is unique to help a human to accomplish complex tasks in a more convenient way. However, long-term following is severely obstructed due to the high-frequency vibration of the quadruped robot and the unevenness of terrains. To solve this problem, a visual approach under a novel T-D-R framework is proposed. The proposed T-D-R framework is composed of a visual tracker based on correlation filter, a person detector with deep learning, and a person re-identification (re-ID) module. The result of the tracker is verified by the detector to improve tracking performance. Especially, the re-ID module is introduced to handle distractions and occlusion caused by other persons, where the convolutional correlation filter (CCF) is employed to discriminate the leader among multiple persons through recording the appearance information in the long run. By comparing the results of the tracker and the detector as well as their similarity scores with the leader identified by the re-ID module, a stable and real-time tracking of the leader can be guaranteed. Experiments reveal that our approach is effective in handling distractions, appearance changes, and illumination variations. A long-distance experiment on a quadruped robot indicates the validity of the proposed approach.