After successful completion of the course, students are able to uniformly formulate photogrammetric and non-photogrammetric observation categories and explain various principles for estimating unknown parameters. In particular, they can relate robust and non-robust estimation methods to assumptions about the nature of observation errors and suggest appropriate estimation methods. They can compare observation types and estimation principles, determine their similarities and explain differences. Students can justify the choice of solution methods for different photogrammetric tasks. They can assess the quality of photogrammetric methods and products. Students can apply projective geometry to photogrammetric problems, explain the concept of incremental fully automated bundle block adjustment and the methods necessary for it, describe dynamic photogrammetric tasks and suggest suitable methods of image processing, especially for the determination of corresponding points, and suitable methods of parameter estimation.

• Mathematical modelling of observations
• Spatial similarity transformation as basic model for observations in images, of control points, in laser scanning, in models (point clouds) and for additional observation types
• Projectiv geometry for image observations and relative orientation
• [ Consideration of map projection and earth curvature in image obseravtions ]
• Parameter estimation
• Estimation of unknown parameters with adjustment calculus
• Estimation of the precision of unknowns and of the observations
• Reliability of observations
• Robust adjustment by weight iteration
• Gross error handling with data snooping
• Random Sample Concensus (RANSAC) al robust method in case of many gross errors
• Quality
• Precision
• Reliability in the photogrammetric context (e.g. measure of overdetermination, forward intersection)
• Consideration of systeatmic errors
• Special methods
• projection matrix, fundamental matrix, essential matrix
• feature point extraction (SIFT)
• incremental bundle block adjustment
• dynamic photogrammetry (optical flow, SLAM)
• strip adjustment of airborne laser scanning data

• oral presentation
• discussion during the lectures
• exemplary computation in accordance with the taught subject
• blackboard and powerpoint

oral examination

during the oral exam (single person of very small groups) students are asked to discuss the requirements and results of different parameter estimation methods, observation types, their errors and their consideration, and finally to explain the special methods

The folliwing topics are expected to be known.

• interior and exterior orientation, collinearity equations, bundle block adjustment
• adjustment of independent observations, estimation of parameters and their precision
• relative orientation of images