Extension and unification of the mathematical background of the basic lectures, concerning photogrammetry, laser scanning and furhter observations with the purpose of geo-referencing hybrid observations.

Increase the ability of judging the quality of the results of adjustments. Knowning methode for parameter estimation in the presence of gross errors in observations.

Learning to judge the strengths and weaknesses of alternative methods for 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

oral exam (may be held in English, however the lecture is given in German)

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, influence of earth curvature on observations