Positioning is needed 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 solving the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interestingly developed positioning techniques is what is called in robotics 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 of SLAM from a geometrical viewpoint is introduced, 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 efficient contribution of deep learning to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality.

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, ranging from passive sensors such as RGB, multispectral, hyperspectral, and thermal as well as active sensors such as light detection and ranging (LiDAR), expounding on their unique functions and gains as far as the detection of pest infestation and disease symptoms is concerned. Indices derived from UAV multispectral and hyperspectral sensors are used to assess their usefulness in vegetation health monitoring and plant physiological changes. Other UAVs are equipped with thermal sensors to identify water stress and temperature anomalies associated with the presence of pests and diseases. Furthermore, the review discusses how LiDAR technology can be used to capture detailed 3D canopy structures as well as volume changes that may occur during the progressing stages of a date palm infection. Besides, the paper examines how machine learning algorithms have been incorporated into remote sensing technologies to ensure high accuracy levels in detecting diseases or pests. This paper aims to present a comprehensive outline for future research focusing on modern methodologies, technological improvements, and direction for the efficient application of UAV-based remote sensing in managing palm tree pests and diseases.

3D modeling of cultural heritage objects like artifacts, statues and buildings is nowadays an important tool for virtual museums, preservation and restoration. In this paper, we introduce a method to automatically design a minimal imaging network for the 3D modeling of cultural heritage objects. This becomes important for reducing the image capture time and processing when documenting large and complex sites. Moreover, such a minimal camera network design is desirable for imaging non-digitally documented artifacts in museums and other archeological sites to avoid disturbing the visitors for a long time and/or moving delicate precious objects to complete the documentation task. The developed method is tested on the Iraqi famous statue “Lamassu”. Lamassu is a human-headed winged bull of over 4.25 m in height from the era of Ashurnasirpal II (883–859 BC). Close-range photogrammetry is used for the 3D modeling task where a dense ordered imaging network of 45 high resolution images were captured around Lamassu with an object sample distance of 1 mm. These images constitute a dense network and the aim of our study was to apply our method to reduce the number of images for the 3D modeling and at the same time preserve pre-defined point accuracy. Temporary control points were fixed evenly on the body of Lamassu and measured by using a total station for the external validation and scaling purpose. Two network filtering methods are implemented and three different software packages are used to investigate the efficiency of the image orientation and modeling of the statue in the filtered (reduced) image networks. Internal and external validation results prove that minimal image networks can provide highly accurate records and efficiency in terms of visualization, completeness, processing time (>60% reduction) and the final accuracy of 1 mm.

In the last two decades, unmanned aircraft systems (UAS) were successfully used in different environments for diverse applications like territorial mapping, heritage 3D documentation, as built surveys, construction monitoring, solar panel placement and assessment, road inspections, etc. These applications were correlated to the onboard sensors like RGB cameras, multi-spectral cameras, thermal sensors, panoramic cameras, or LiDARs. According to the different onboard sensors, a different mission plan is required to satisfy the characteristics of the sensor and the project aims. For UAS LiDAR-based mapping missions, requirements for the flight planning are different with respect to conventional UAS image-based flight plans because of different reasons related to the LiDAR scanning mechanism, scanning range, output scanning rate, field of view (FOV), rotation speed, etc. Although flight planning for image-based UAS missions is a well-known and solved problem, flight planning for a LiDAR-based UAS mapping is still an open research topic that needs further investigations. The article presents the developments of a LiDAR-based UAS flight planning tool, tested with simulations in real scenarios. The flight planning simulations considered an UAS platform equipped, alternatively, with three low-cost multi-beam LiDARs, namely Quanergy M8, Velodyne VLP-16, and the Ouster OS-1-16. The specific characteristics of the three sensors were used to plan flights and acquired dense point clouds. Comparisons and analyses of the results showed clear relationships between point density, flying speeds, and flying heights.

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.