Multi-Objective Convolutional Neural Networks for Robot Localisation and 3D Position Estimation in 2D Camera Images
| Autor: | Jim Torresen, Kyrre Glette, Inka Brijacak, Saeed Yahyanejad, Ole Jakob Elle, Justinas Miseikis |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Rest (physics) 0209 industrial biotechnology Relation (database) Computer science business.industry Deep learning Collaborative robotics 02 engineering and technology Base (topology) 01 natural sciences Convolutional neural network Field (computer science) Computer Science - Robotics 020901 industrial engineering & automation Position (vector) 0103 physical sciences Robot Computer vision Artificial intelligence business Robotics (cs.RO) 010301 acoustics |
| Zdroj: | UR |
| DOI: | 10.1109/urai.2018.8441813 |
| Popis: | 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. Ubiquitous Robots 2018 Regular paper submission |
| Databáze: | OpenAIRE |
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