The simultaneous localization and mapping (SLAM):An overview

Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality

Potential use of drone ultra-high-definition videos for detailed 3D city modeling

Ongoing developments in video resolution either using consumer-grade or professional cameras has opened opportunities for different applications such as in sports events broadcasting and digital cinematography. In the field of geoinformation science and photogrammetry, image-based 3D city modeling is expected to benefit from this technology development. Highly detailed 3D point clouds with low noise are expected to be produced when using ultra high definition UHD videos (e.g., 4K, 8K). Furthermore, a greater benefit is expected when the UHD videos are captured from the air by consumer-grade or professional drones. To the best of our knowledge, no studies have been published to quantify the expected outputs when using UHD cameras in terms of 3D modeling and point cloud density. In this paper, a quantification is shown about the expected point clouds and orthophotos qualities when using UHD videos from consumer-grade drones and a review of which applications they can be applied in. The results show that an improvement in 3D models of ≅65% relative accuracy and ≅90% in point density can be attained when using 8K video frames compared with HD video frames which will open a wide range of applications and business cases in the near future

Adjustment Models in 3D Geomatics and Computational Geophysics

Adjustment Models in 3D Geomatics and Computational Geophysics: With MATLAB Examples, Volume Four introduces a complete package of theoretical and practical subjects in adjustment computations relating to Geomatics and geophysical applications, particularly photogrammetry, surveying, remote sensing, GIS, cartography, and geodesy. Supported by illustrating figures and solved examples with MATLAB codes, the book provides clear methods for processing 3D data for accurate and reliable results. Problems cover free net adjustment, adjustment with constraints, blunder detection, RANSAC, robust estimation, error propagation, 3D co-registration, image pose determination, and more

Crowdsource drone imagery: A powerful source for the 3D documentation of cultural heritage at risk

Heritage at risk is a terminology used to describe the sites that are highly at risk of being lost as a result of intentional demolition, deterioration, negligence or subject to improper preservation or mistreatment. Iraq is one of the countries that suffered in the last decade from intentional demolition of highly valuable heritage sites and objects. As Iraq gradually recovering from wars and violence with limited resources and budgets, historical and heritage places are still at risk because of neglect, community ignorance, insufficient planning, and military actions. Therefore, in this paper, we propose the idea of using crowdsource drone images and videos which are captured by amateurs for the documentation of heritage sites. Those crowdsource images represent a great source of data that does not require significant financial and hard labor resources. It should be noted that there is no guarantee to have the captured data being sufficient for the 3D documentation and therefore it is proposed to integrate, when possible, multiple captured crowdsource data to ensure complete documentation. In this paper, three Iraqi historical sites are 3D reconstructed using crowdsource drone videos, namely: Rabban Hormizd Monastery (AD 640), Taq Kasra (AD 242 to 272), and the Great Mosque of Samarra (AD 849–851). The experiments showed a successful 3D modeling of the three mentioned heritage objects using the crowdsource drone video images despite being captured for non-3D purposes which require high expertise and planning. With the absence of highly accurate reference data, the overall relative accuracy of the object’s dimensions is found to be less than 1 m