Title: Robot Localisation and 3D Position Estimation Using a Free-Moving Camera and Cascaded Convolutional Neural Networks,
Abstract: Many works in collaborative robotics and human-robot interaction focuses on
identifying and predicting human behaviour while considering the information
about the robot itself as given. This can be the case when sensors and the
robot are calibrated in relation to each other and often the reconfiguration of
the system is not possible, or extra manual work is required. We present a deep
learning based approach to remove the constraint of having the need for the
robot and the vision sensor to be fixed and calibrated in relation to each
other. The system learns the visual cues of the robot body and is able to
localise it, as well as estimate the position of robot joints in 3D space by
just using a 2D color image. The method uses a cascaded convolutional neural
network, and we present the structure of the network, describe our own
collected dataset, explain the network training and achieved results. A fully
trained system shows promising results in providing an accurate mask of where
the robot is located and a good estimate of its joints positions in 3D. The
accuracy is not good enough for visual servoing applications yet, however, it
can be sufficient for general safety and some collaborative tasks not requiring
very high precision. The main benefit of our method is the possibility of the
vision sensor to move freely. This allows it to be mounted on moving objects,
for example, a body of the person or a mobile robot working in the same
environment as the robots are operating in.