Master Thesis Project – Unsupervised learning for joint Visual Odometry and Depth Estimation

Key Skills

Computer Vision (CV), Deep Learning, Design, Image processing, Project management, Python Programming

Job Description

By using Deep Learning for jointly estimating Ego Motion and Depth and comparing the performance with current approaches we increase our understanding of the potential, and to what degree this approach is beneficial for our products.

Also, by not using another sensor as reference, sensor specific artifacts can be avoided.

Learning Objectives

• Applying a machine learning approach to two classical image processing problems.
• Improved knowledge with ego motion and depth estimation using deep learning and understanding of how performance is affected.
• Possible extension to consider also rolling shutter effects.

The first part of the Master Thesis should include a study of deep learning based visual odometry and depth estimation approaches in academia. Based on this, the student will, together with the supervisors, decide on a suitable method to evaluate, both on Arriver data and on a public dataset. 

Student Background

Master studies, experience with python, Deep Learning, and Computer Vision.

Thesis work suitable for 1-2 students.

Location: Linköping & Stockholm

*References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.

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