TTK4255 - Robotic Vision


Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Home exam 25/100 4 hours
Approved report 75/100

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%).

Specific conditions

Compulsory activities from previous semester may be approved by the department.

Required previous knowledge

TMA4245 - Statistikk, TTK4115 - Linear System Theory

Course materials

Information on this is given at the start of the semester.

More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  SPRING 2022

Language of instruction: English

Location: Trondheim

Subject area(s)
  • 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
Department of Engineering Cybernetics


Examination arrangement: Portfolio assessment

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD Approved report 75/100
Room Building Number of candidates
Spring ORD Home exam (1) 25/100





Room Building Number of candidates
Summer UTS Approved report 75/100
Room Building Number of candidates
Summer UTS Home exam 25/100





Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
  • 1) Merk at eksamensform er endret som et smittevernstiltak i den pågående koronasituasjonen.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU