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, and assignments. 75% of the assignments must be approved to enter the final exam.

Compulsory assignments

• Assignments

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

At least one of the following subjects (or equivalent from other universities):

TTK4115 Linear System Theory,

TTT4275 Estimation, Detection and Classification,

TMA4268 Statistical Learning

TMA4267 Linear Statistical Models or

TMA4245/TMA4240 - Statistics

Course materials

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