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dc.contributor.authorFahmy, Ahmed
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.description.abstractThere is a growing demand over the last two decades for generating digital surface models (DSMs) in real-time. One of the important applications for real-time DSMs is aircraft landing, especially in a degraded visual environment. In such a challenging environment, no clear visual conditions exist, which can potentially result in the loss of equipment and/or life. These conditions can be caused by snow, rain, blowing sand and dust, fog, smoke, clouds, darkness, and flat light conditions that currently hamper aviation operations. With the growing demand for such applications, there is a need for efficient LiDAR processing algorithms capable of generating DSMs in real-time and provide the safe landing zone (SLZ) for the aircraft landing. Several research activities have addressed robust filtering algorithms for the airborne laser scanning (ALS) data. Although most of these filtering algorithms are accurate and robust, they are limited to post-processing since they rely on computationally expensive algorithms and require high execution time that is not suitable for real-time applications. The aim of this research is to design and implement an efficient algorithm that can filter LiDAR point cloud, generate DSM, slope map, roughness map and operate in real-time. The algorithm is suitable for real-time implementation on limited resources embedded-processors without the need for a supercomputer. It is also capable of using all the generated maps in identifying SLZs suitable for the aircraft landing. The method suggested in this research identifies the best SLZ for the aircraft based on processing the 3D LiDAR point cloud collected from a LiDAR mounted on the aircraft. The iii proposed method filters these data, then constructs a DSM of the terrain with high accuracy. It also generates a slope map, roughness map and binary map, which are used to determine the best SLZ for this aircraft. The SLZ decision is made according to the permissible slope and roughness values in which the aircraft can land safely. Moreover, the proposed method filters SLZ to include only those that have enough space for the aircraft landing. The proposed method was successfully implemented in C++ in real-time and was examined on a professional software flight simulation. With comparison to the reference data, we were able to demonstrate the capability of the developed method to identify SLZs for helicopters assisting pilots to decide on the safest landing area. The proposed method was also able to distinguish, in real-time, the roofs of the buildings (areas of low slope) from the edges of the same buildings (areas of high slope).en_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsAttribution-NonCommercial 3.0 United States*
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectAirborne Laser Scanningen_US
dc.subjectDigital Surface Modelen_US
dc.subjectLiDAR Point Clouden_US
dc.subjectReal-Time LiDAR Data Processingen_US
dc.subjectSlope Mapen_US
dc.titleReal-Time Realization of Lidar – Based Digital Surface Models for Airborne Safe Landing Zone Identification in Challenging Environmentsen_US
dc.description.degreeMaster of Applied Scienceen_US
dc.contributor.supervisorNoureldin, Aboelmagd
dc.contributor.supervisorGivigi, Sidney
dc.contributor.departmentElectrical and Computer Engineeringen_US

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial 3.0 United States