Course - Robotic Vision - TTK4255
Robotic Vision
About
About the course
Course content
Elements of Visual Perception, Image Sampling and Quantization, related Mathematical tools applied to Image Processing and Analysis (array, matrix, linear and non-linear operations, arithmetic and geometric operations, morphology, spatial and temporal operations, frequency analysis, linear algebra, probabilistic methods, image transformations and geometric operations) Image Formation: Camera Models, Calibration, Single view geometry, Multiple view geometry, Epipolar geometry, Feature extraction, Bundle adjustment Position and Orientation: Feature based alignment; Pose estimation; Time varying pose and trajectories, Structure from motion, dense Motion Estimation, Visual Odometry (Semi-direct VO, direct sparse odometry) Localization and Mapping: Initialization, Tracking, Mapping, geometric SLAM formulations (indirect vs. direct error formulation, geometry parameterization, sparse vs. dense model, optimization approach), Relocalization and map Optimization, Examples: Indirect (Feature based) methods (MonoSLAM, PTAM, ORB-SLAM), Direct methods (DTAM, LSD-SLAM), Sensor combinations (IMU, mono vs. Stereo, RGB-Depth), Analysis and parameter studies Recognition and Interpretation: Object detection, Instance recognition, Category recognition, Context and Scene understanding
Learning outcome
Knowledge: Knowledge about core applications in Robotic Vision. Knowledge about fundamental (physical) concepts about visual perception. Knowledge about image formation, image representation and camera models. Knowledge about image sampling, quantization and processing. Knowledge about structure from motion concepts for pose, tracking, motion estimation as well as visual odometry (VO) simultaneous localization an mapping (SLAM) strategies exploring popular methods. Basic knowledge about feature extraction, object recognition, context awareness/semantics and scene understanding. Skills: Be able to choose imaging systems with respect to specific applications. Calibrate the imaging system. Modify different imaging setups with respect to environmental conditions. Manipulate and implement pose, tracking and motion estimation techniques. Implement, tune and evaluate SLAM alorithms. Implement object recognition and classification methods. At the end of the semester a successful student should have skills in processing and analysis of digital images and be able to design simple robot vision and machine vision systems. General competence: Be able to apply the fundamental imaging principles. Consciousness about the role of visual sensing in robotic applications. Be able to analyze strength and weaknesses of different vision based approaches.
Learning methods and activities
The course is given as a mixture of lectures, assignments and two projects. All assignments and projects must be approved to enter the final exam.
Compulsory assignments
- Exercises
Further on evaluation
Portfolio evaluation ("Mappevudering") is used to define the final grade in the subject. Parts of the portfolio are the final exam in writing 80%, and a project report (lab experiment findings) 20%. The result of each part is given in percentage units, while evaluation of the entire portfolio (the final grade) is given as a letter. If there is a re-sit examination, the examination form may change from written to oral. Both project report (20%) need to be retaken in addition to the main exam (80%).
Recommended previous knowledge
Calculus 1, 2, 3 and 4 (TMA4100, TMA4105, TMA4115, TMA4120), TTK4105 Control Systems, TTT4275 - Estimering, deteksjon og klassifisering,TTT4120 - Digital signalbehandling, TDT4195 - Grunnleggende visuell databehandling or a comparable background
Required previous knowledge
TMA4245 - Statistikk, TTK4115 - Linear System Theory
Course materials
Information on this is given at the start of the semester.
Subject areas
- Computer and Information Science
- Marine Cybernetics
- ICT and Mathematics
- Computer Science
- Applied and Industrial Mathematics
- Graphics/Image Processing
- Medical Computer Science
- Signal Processing
- Multivariate Image Analysis
- Numerical Mathematics
- Aqua Culture
- Process Automation
- Applied Mechanics - Fluid Mechanics
- Photogrammetry
- Engineering Cybernetics
- Optics
- Bildediagnostikk
- Information Technology and Informatics
- Mathematics
- Statistics
Contact information
Course coordinator
Department with academic responsibility
Examination
Examination
Ordinary examination - Spring 2022
Approved report
Home exam (1)
Submission 2022-05-27 Time Release 09:00
Submission 13:00 Duration 4 hours Exam system Inspera Assessment
- Other comments
- 1) Merk at eksamensform er endret som et smittevernstiltak i den pågående koronasituasjonen.