As Austria's largest research and technology organisation for applied research, we are dedicated to make substantial contributions to solving the major challenges of our time, climate change and digitisation. To achieve our goals, we rely on our specific research, development and technology competencies, which are the basis of our commitment to excellence in all areas. With our open culture of innovation and our motivated, international teams, we are working to position AIT as Austria's leading research institution at the highest international level and to make a positive contribution to the economy and society.
 
Our Center for Vision, Automation & Control located in Vienna invites applications for a master’s thesis position. At the Center for Vision, Automation & Control our research unit “Assistive & Autonomous Systems” closely works with industrial partners in order to develop safe and reliable technology components for assistance systems, that can be used in various areas of applications, for e.g.: Railway / Public Transport, Construction Industry, Logistics & Transportation, Agriculture & Forestry as well as in Aviation. 
 
Automated robotic systems perceive the environment and can classify, locate and track objects for various applications in domain-specific areas. As part of our team Assistive & Autonomous Systems, you will support us within our research project MoSaiC, focusing on real-time monitoring of CBRN events and innovative sampling technologies to enhance the dynamic mapping of threats and vulnerabilities.

• Throughout this master’s thesis, you will closely work with our researchers and engineers, who focus on the development and deployment of assistive and autonomous systems, particularly unmanned aircrafts and land-based mobile machines – Hereby you will communicate and engage with a multi-disciplinary team to solve diverse multi-facetted challenges.
• Under the guidance of our experts, you will contribute to a rapidly growing field in aerial robotics.
• You will investigate the state-of-the-art and apply it in real-world scenarios and – as a final step – will design a proof-of-concept demonstration.
• You will learn how to implement an error propagation module to track the pose uncertainty of the LiDAR odometry.
• You will develop Deep Learning methods to build point cloud descriptors fit for loop detection.

• You investigate the literature about local vs global descriptors and contribute to the project development.
•  You integrate the resulting Loop Detections in a robust 6DoF Loop Closure module.
• You leverage the Loop Closure events to correct the accumulated uncertainty.
• You may present the evaluated and documented concepts and test runs at a conference or publish them in a journal.
• You will become a member of our indoor UAV development and investigate the best solutions for drift and map correction for LiDAR data.