Multi-Objective Convolutional Neural Networks for Robot Localisation and 3D Position Estimation in 2D Camera Images

TL;DR

This paper introduces a multi-objective deep learning method that localizes and estimates 3D positions of robots in 2D camera images, enabling flexible camera setups without fixed calibration.

Contribution

It presents a novel multi-objective CNN that simultaneously identifies robot type, localizes it, and estimates 3D positions, improving flexibility over fixed setup systems.

Findings

Accurately localizes robots in 2D images.

Estimates 3D positions of robot base and joints.

Outperforms previous cascaded CNN approach.

Abstract

The field of collaborative robotics and human-robot interaction often focuses on the prediction of human behaviour, while assuming the information about the robot setup and configuration being known. This is often the case with fixed setups, which have all the sensors fixed and calibrated in relation to the rest of the system. However, it becomes a limiting factor when the system needs to be reconfigured or moved. We present a deep learning approach, which aims to solve this issue. Our method learns to identify and precisely localise the robot in 2D camera images, so having a fixed setup is no longer a requirement and a camera can be moved. In addition, our approach identifies the robot type and estimates the 3D position of the robot base in the camera image as well as 3D positions of each of the robot joints. Learning is done by using a multi-objective convolutional neural network with four previously mentioned objectives simultaneously using a combined loss function. The multi-objective approach makes the system more flexible and efficient by reusing some of the same features and diversifying for each objective in lower layers. A fully trained system shows promising results in providing an accurate mask of where the robot is located and an estimate of its base and joint positions in 3D. We compare the results to our previous approach of using cascaded convolutional neural networks.