Online Visual Robot Tracking and Identification using Deep LSTM Networks
This addresses robot collaboration challenges, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles the problem of mutual tracking and identification for collaborative robots with identical appearance by presenting a real-time vision-based pipeline using deep LSTM networks, achieving promising results on synthetic and real datasets with long-term occlusions.
Collaborative robots working on a common task are necessary for many applications. One of the challenges for achieving collaboration in a team of robots is mutual tracking and identification. We present a novel pipeline for online visionbased detection, tracking and identification of robots with a known and identical appearance. Our method runs in realtime on the limited hardware of the observer robot. Unlike previous works addressing robot tracking and identification, we use a data-driven approach based on recurrent neural networks to learn relations between sequential inputs and outputs. We formulate the data association problem as multiple classification problems. A deep LSTM network was trained on a simulated dataset and fine-tuned on small set of real data. Experiments on two challenging datasets, one synthetic and one real, which include long-term occlusions, show promising results.