Master Thesis deep Learning for Lidar-based Slam

vor 5 Monaten

Wien, Österreich Austrian Institute of Technology Vollzeit

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.

MASTER THESIS "DEEP LEARNING FOR LIDAR-BASED SLAM LOOP CLOSURE"

CENTER FOR VISION, AUTOMATION & CONTROL
- 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 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.

**Your qualifications as an Ingenious Partner**:

- You are enrolled in a master’s study programme in computer science, software engineering, information and computer engineering, mathematics or similar
- Experience in programming / script languages: Python, C++ (PCL, Ceres, GTSAM)
- Experience in point cloud processing
- Experience in ROS and Gazebo is highly advantageous
- Experience with Deep Learning is highly advantageous
- Experience with SLAM is highly advantageous
- Good knowledge of verbal and written English

**What to expect**:

- **Duration of the master’s thesis project**: 6 months
- **Start date**: ideally as soon as possible - However there is some flexibility concerning the start-date EUR 968,
- gross per month for 20 hours/week based on the collective agreement. There will be additional company benefits. As a research institution, we are familiar with the supervision and execution of master theses, and we are looking forward to supporting you accordingly

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