LEARNING OUTCOMES
Basic purpose of this course is the familiarization of students with state-of-the-art methods for collecting 3D geospatial data of high resolution and in large scales (close-range), with the emphasis mainly on applications using moving recording platforms (mobile mapping) as well sensors on UAVs. The student will have the opportunity to plan such missions, follow the data collection steps and primarily to better understand the photogrammetric processes for producing 3D models from such imagery. Successful completion of this course means that students:
- Have understood and are able to describe, analyze and compare the particular types of UAVs used in precision mapping (pros – cons) as well as their systems of recording, mapping and navigation.
- Are able to describe, analyze and compare the different Mobile Mapping Systems (MMS) and the operation principles of their mapping sensors.
- Are in position to design UAV recording flights and MMS mapping projects and ecaluate the quality of data provided be the sensors.
- Ara able to calibrate the optical sensors of these systems either by using calibration fields or during the recording phase.
- Can implement all individual steps of a photogrammetric mapping process using UAV platforms or MMSs (data collection, their processing and analysis, production of final 3D products and of metric textured projections, interpretation and evaluation of results).
- Are capable of applying Structure-from-Motion solutions in combination with synchronous GPS/INS observations or ground control point (GCP) measurements.
- Have understood and are capable of describing, analyze and compare the methods of SLAM, visual SLAM and visual odometry, and further they can apply them on video sequences from UAV platforms and MMS using open-source software.
General Competences
- Search for, analysis and synthesis of data and information, with the use of the necessary technology
- Working independently
- Teamwork
- Production of free, creative and inductive thinking
- Working in an interdisciplinary environment
SYLLABUS
Analysis of stat-of-the-art technologies in mobile mapping systems and UAV. Categorization according to system type, operation principle, automated navigation (GNSS navigation, inertial systems INS-IMU, optical data obstacle avoidance), sensor types and data collected. Basic principles of planning for data recording with UAVs (fixed-wing and drone) and mobile mapping systems. Sensor calibration. Techniques of optical navigation (SLAM, visual odometry). Photogrammetric data processing – automatic image orientation using extracted interest points and combined GPS-INS observations, introduction of ground control points and accuracy assessment, image-based generation of 3D models via dense matching techniques. Application for generation of 3D city and/or building models. Combination/comparison with data from laser scanning and terrestrial images. Triangulation from mobile mapping systems – accuracy of results. αποτελεσμάτων. Automatic digitization of road signs.
STUDENT PERFORMANCE EVALUATION
Language of evaluation: Greek
Methods of Evaluation:
• Evaluation of performance in the Lab exercises.
• Oral presentation of project.
SUGGESTED BIBLIOGRAPHY
1. Course Notes and Slides (in Greek)
2. Szeliski R., 2010. Computer Vision: Algorithms and Application. Springer (http://szeliski.org/Book/)
3. Hartley R., Zisserman A., 2000. Multiple View Geometry in Computer Vision. Cambridge Univ. Press
4. Fӧrstner W., Wrobel B. P., 2016. Photogrammetric Computer Vision. Springer