Ehsan Khoramshahi defends his PhD thesis on Multi-Projective Camera-Calibration, Modeling, and Integration in Mobile-Mapping Systems

1.12.2020
On Monday the 14th of December 2020, M.Eng. Ehsan Khoramshahi will defend his doctoral thesis on Multi-Projective Camera-Calibration, Modeling, and Integration in Mobile-Mapping Systems. The thesis is related to research done at the Finnish Geospatial Research Institute FGI of the National Land Survey of Finland (NLS).

M.Eng. Ehsan Khoramshahi defends his doctoral thesis Multi-Projective Camera-Calibration, Modeling, and Integration in Mobile-Mapping Systems on Monday the 14th of December 2020 at 14 o'clock in the University of Helsinki Exactum Building, Auditorium CK112 (Pietari Kalmin katu 5, basement). His opponent is Professor Janne Heikkilä (University of Oulu, Finland) and custos Professor Petri Myllymäki (University of Helsinki). The defence will be held in English. It is possible to follow the defence as a live stream at https://helsinki.zoom.us/j/63563369057?pwd=UXpteWJDMlFEY2V5cnU1SlhYbGZKUT09.

The thesis of Ehsan Khoramshahi is related to research done at the Finnish Geospatial Research Institute FGI of the National Land Survey of Finland (NLS). His supervisors have been Research Professor Eija Honkavaara (Finnish Geospatial Research Institute FGI), and Professor Petri Myllymäki and Assistant Professor Arto Klami (University of Helsinki).

Multi-Projective Camera-Calibration, Modeling, and Integration in Mobile-Mapping Systems

Optical systems are vital parts of most modern systems such as mobile mapping systems, autonomous cars, unmanned aerial vehicles (UAV), and game consoles. Multi-camera systems (MCS) are commonly employed for precise mapping including aerial and close-range applications.

In the first part of this thesis a simple and practical calibration model and a calibration scheme for multi-projective cameras (MPC) is presented. The calibration scheme is enabled by implementing a camera test field equipped with a customized coded target as FGI’s camera calibration room. The first hypothesis was that a test field is necessary to calibrate an MPC. Two commercially available MPCs with 6 and 36 cameras were successfully calibrated in FGI’s calibration room. The calibration results suggest that the proposed model is able to estimate parameters of the MPCs with high geometric accuracy, and reveals the internal structure of the MPCs.

In the second part, the applicability of an MPC calibrated by the proposed approach was investigated in a mobile mapping system (MMS). The second hypothesis was that a system calibration is necessary to achieve high geometric accuracies in a multi-camera MMS. The MPC model was updated to consider mounting parameters with respect to GNSS and IMU. A system calibration scheme for an MMS was proposed. The results showed that the proposed system calibration approach was able to produce accurate results by direct georeferencing of multi-images in an MMS. Results of geometric assessments suggested that a centimeter-level accuracy is achievable by employing the proposed approach. A novel correspondence map is demonstrated for MPCs that helps to create metric panoramas.

In the third part, the problem of real-time trajectory estimation of a UAV equipped with a projective camera was studied. The main objective of this part was to address the problem of real-time monocular simultaneous localization and mapping (SLAM) of a UAV. An angular framework was discussed to address the gimbal lock singular situation. The results suggest that the proposed solution is an effective and rigorous monocular SLAM for aerial cases where the object is near-planar. In the last part, the problem of tree-species classification by a UAV equipped with two hyper-spectral an RGB cameras was studied. The objective of this study was to investigate different aspects of a precise tree-species classification problem by employing state-of-art methods. A 3D convolutional neural-network (3D-CNN) and a multi-layered perceptron (MLP) were proposed and compared. Both classifiers were highly successful in their tasks, while the 3D-CNN was superior in performance. The classification result was the most accurate results published in comparison to other works.

Availability of the dissertation

An electronic version of the doctoral dissertation is available on the e-thesis site of the University of Helsinki at http://urn.fi/URN:ISBN:978-951-51-6846-7.

Printed copies will be available on request from Ehsan Khoramshahi: ehsan.khoramshahi@helsinki.fi