Mobile Mapping Systems

The restoration process of the Sanctuary of the Beata Vergine del Trompone in Moncrivello (VC) represents a precious opportunity for training and experimentation of several lines of research. In particular, in anticipation of an imminent intervention, aimed at the preservation of the existing building, a systematic and accurate survey campaign is carried out in order to apply the Heritage Building Information Modelling methodology at the whole religious complex. This strategy allows reconstructing, with the help of the data coming from archival research, the historical building yard and the evolving stages of the complex, highlighted by virtual reproduction.

11 th International Conference on Cryptography and Information Security (CRYPIS 2022) focuses on cutting-edge results in Applied Cryptography and Information security. It aims to bring together scientists, researchers and students to exchange novel ideas and results in all aspects of Cryptography, Coding and Information security. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

Three Dimensional (3D) models are widely used in clinical applications, geosciences, cultural heritage preservation, and engineering; this, together with new emerging needs such as building information modeling (BIM) develop new data capture techniques and devices with a low cost and reduced learning curve that allow for non-specialized users to employ it. This paper presents a simple, self-assembly device for 3D point clouds data capture with an estimated base price under €2500; furthermore, a workflow for the calculations is described that includes a Visual SLAM-photogrammetric threaded algorithm that has been implemented in C++. Another purpose of this work is to validate the proposed system in BIM working environments. To achieve it, in outdoor tests, several 3D point clouds were obtained and the coordinates of 40 points were obtained by means of this device, with data capture distances ranging between 5 to 20 m. Subsequently, those were compared to the coordinates of the same t...

Three Dimensional (3D) models are widely used in clinical applications, geosciences, cultural heritage preservation, and engineering; this, together with new emerging needs such as building information modeling (BIM) develop new data capture techniques and devices with a low cost and reduced learning curve that allow for non-specialized users to employ it. This paper presents a simple, self-assembly device for 3D point clouds data capture with an estimated base price under 2500; furthermore, a workflow for the calculations is described that includes a Visual SLAM-photogrammetric threaded algorithm that has been implemented in C++. Another purpose of this work is to validate the proposed system in BIM working environments. To achieve it, in outdoor tests, several 3D point clouds were obtained and the coordinates of 40 points were obtained by means of this device, with data capture distances ranging between 5 to 20 m. Subsequently, those were compared to the coordinates of the same targets measured by a total station. The Euclidean average distance errors and root mean square errors (RMSEs) ranging between 12-46 mm and 8-33 mm respectively, depending on the data capture distance (5-20 m). Furthermore, the proposed system was compared with a commonly used photogrammetric methodology based on Agisoft Metashape software. The results obtained demonstrate that the proposed system satisfies (in each case) the tolerances of 'level 1' (51 mm) and 'level 2' (13 mm) for point cloud acquisition in urban design and historic documentation, according to the BIM Guide for 3D Imaging (U.S. General Services).

Various researches and technologies are being developed and utilized to improve the function of autonomous vehicle. These systems have been developed from simple systems that assisted drivers for safe driving. More recently, an autonomous vehicle, which can be driven without driver with control the steering wheel through the vehicle object recognition, has passed a test driving and is driving on real roads. To travel on the public road, it is necessary to recognize road facilities and have positioning technology, which are the basic technologies for the safe driving of autonomous vehicle. The target facilities to be recognized by autonomous vehicles are medians, curbs, and lanes among facilities that must be recognized for the safe driving of autonomous vehicle. In particular, the recognition of road facilities should be applicable under various conditions to reflect actual driving environments and accuracy at a certain level should be guaranteed. In this study a research is conducted on the mapping of road facilities using Light Detection and Ranging (LiDAR) and the extraction of information of road facilities. Surrounding information was mapped using LiDAR data acquired while driving vehicle, and road facility information was obtained using mapped PointCloud.

With a focus on mobile and web mapping, we propose several algorithms for on-the-fly generalization of point data, such as points of interest (POIs) or large point collections. In order to achieve real-time performance we use a quadtree. With their hierarchical structure and progressive levels of detail, indexes of the quadtree family lend themselves as auxiliary data structures to support algorithms for generalization operations , including selection, simplification, aggregation, and displacement of point data. The spatial index can further be used to generate several measures on the relative point position and proximity of points, neighborhood, clusters, local and global density, etc. Using the tiles of the tree structure, such measures can be efficiently generated and updated. These measures can then serve to make educated guesses on the density and proximity of points across a series of map scales, and thus enable to control the operation of the generalization algorithms. An implementation of the proposed algorithms has shown that thanks to the quadtree index, real-time performance can be achieved even for large point sets. Furthermore, the quadtree data structure can be extended into a storage structure , which can be used to store pre-computed generalizations; thus, a desired level of detail can simply be retrieved from storage.

Mobile Mapping Systems (MMS) can be employed for road safety inspection purposes to obtain Global Positioning System data and achieve accurate image data of the side features. Utilizing the MMS data, this paper develops a two-phase automated methodology to evaluate the location and numerical value of the speed limit signs before horizontal curves on two-lane rural roadways. The first phase includes the collection of the GPS data, including the horizontal curve radius, the associated speed, curve starting point, and the safe distance before the curve to install the speed limit sign and its' value. In the second phase, the required features on speed limit signs are extracted using a Support Vector Machine (SVM)-based pattern recognition method. Findings indicate that the proposed methodology leads to 92% and 97% accuracy while identifying the speed limit signpost itself in a frame and distinguishing the speed value on the sign, respectively. In addition, horizontal curve beginning points and curve radii have been identified with a precision of 97% and 90%, respectively. The proposed methodology has the potential to help attain the needed safety standards related to the location and value on the speed limit signs before the horizontal curves in a low-cost manner.