Planes are basic primitives in man-made environments that could be extracted from 3D data (LiDARs, depth cameras). Those primitives can serve as landmarks in SLAM for localization and mapping of autonomous systems (self-driving cars, indoor robots – service robots, warehouse robots). Plane SLAM project is devoted to research on these primitives in two directions: (1) develop methods for plane extraction from heterogeneous sensors and (2) elaborate on how those primitives could be used in SLAM backend.
To get intuition on SLAM and its application in robotics: https://ieeexplore.ieee.org/document/7747236 About this topic in Russian: https://drive.google.com/file/d/15Mn4ZdC4TOcF0fuU1EFhdj9VYEKhgScK/view?usp=sharing
Current state: we have a benchmark of existing plane extraction approaches for depth camera and LiDAR, that includes labeled dataset, metrics, performance evaluation of existing methods for plane extraction.
All topics related to this project could be considered as an opportunity to be on board for publications in top-level robotics conferences and journals (in case of generating smth useful for project, ofc).
Key members of the project from SPBU side: Dmitrii Iarosh (M.Sc., 1st year), Pavel Mokeev (B.Sc., 2nd year)
The task is to evaluate performance of existing planar SLAM backends on our dataset. That includes the next subtasks:
Planar SLAM backends to be evaluated (not limited to this list)
Technologies used in project: Python, C++, Docker, Bash, CI/CD (GA)
Experience with any mentioned technologies will be a great bonus, but we don't expect that students should have a strong background in them, the most important thing is to be open to work intensely and hard, don't be scared a lot from math.
2 курс, 3 курс, Бакалаврская ВКР
Литвинов Юрий Викторович
Kornilova Anastasiia Валерьевна
Mobile Robotics Lab, Skoltech