Thesis - Instance segmentation using 2.5D data

Instance segmentation is useful in many applications, like for example robot picking. The state of the art deep learning methods gives impressive results, using gray or RGB data. However, it gets more and more common with cameras also giving depth (2.5D), which is usually necessary for robust robot picking.

The goal of this thesis is to develop deep learning instance segmentation networks including depth data and to evaluate how the performance is affected by incorporating the depth.

The work will be done in close cooperation with our Robot Guidance team, and a resulting implementation can be verified on real applications.

Prerequisites

• Computer Vision & Machine Learning knowledge
• Programming skills

Contact

For more information about the position, contact:

Thomas Berglund, R&D Manager, thomas.berglund@sick.se

or

Charlotte Axelsson, HR Manager, charlotte.axelsson@sick.se

Welcome with your application 14^th of October at the latest!

SICK is a world-leading supplier of sensors and sensor solutions for industrial applications. We are 11 000 employees in 50 countries and our headquarter is located in Freiburg, Germany. SICK in Linköping is an innovation center for Machine Vision and we are 80 committed employees with a big interest in image processing and visualization. For more than 35 years, our team at SICK Linköping has successfully developed and delivered software for technically leading products within the field of 2D and 3D vision, as well as system solutions for i.e. robot guidance and quality control.

At SICK in Linköping, we are very proud of being a healthy and attractive workplace. For many years, we have been elected as one of the best workplaces in Sweden according to the survey Great Place to Work, the latest award is from 2021. We work actively to reduce our climate footprint and we are active in various ways to contribute to the society and to increase diversity at our workplace.