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McGill Mobile Robotics Lab
Underwater Multi-Robot Convoying Using Visual Tracking by Detection

Video demonstration of robot convoying during field trials at the McGill Bellairs Research Institute in Barbados.
We present a robust multi-robot convoying approach relying on visual detection of the leading agent, thus enabling target following in unstructured 3D environments. Our method is based on the idea of tracking by detection, which interleaves image-based position estimation via temporal filtering with efficient model-based object detection. This approach has the important advantage of mitigating tracking drift (i.e. drifting out of the view of the target), which is a common symptom of model-free trackers and is detrimental to sustaining convoying in practice. To illustrate our solution, we collected extensive footage of an underwater swimming robot in ocean settings, and hand-annotated its location in each frame. Based on this dataset, we present an empirical comparison of multiple tracker variants, including the use of several Convolutional Neural Networks both with and without recurrent connections, as well as frequency-based model-free trackers. We also demonstrate the practicality of this tracking-by-detection strategy in real-world scenarios, by successfully controlling a legged underwater robot in five degrees of freedom to follow another robot's arbitrary motion.