TTK4255 - Robotic Vision


Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
Approved report 20/100
Written examination 80/100 4 hours D

Course content

Elements of Visual Perception: Light and the Electromagnetic Spectrum, Visual Sensors, Image Sensing and Acquisition in Air and Underwater, 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, 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 in terms of electromagnetic radiation and mixtures of light as continuous spectra. Knowledge about imaging systems and image acquisition techniques in different media. Knowledge about image sampling, quantization and representation. Knowledge about pose, tracking, motion estimation techniques as well as 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 a
laboratory project. 80% of the assignments and the laboratory report
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

Exam registration requires that class registration is approved in the same semester. 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 2021

No.of lecture hours: 3
Lab hours: 2
No.of specialization hours: 7

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 form Weighting Examination aids Date Time Digital exam Room *
Spring ORD Approved report 20/100
Room Building Number of candidates
Spring ORD Written examination 80/100 D
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.

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

More on examinations at NTNU