A Self-Assembly Portable Mobile Mapping System for Archeological Reconstruction Based on VSLAM-Photogrammetric Algorithm

A Review of Mobile Mapping Systems: From Sensors to Applications

The evolution of mobile mapping systems (MMSs) has gained more attention in the past few decades. MMSs have been widely used to provide valuable assets in different applications. This has been facilitated by the wide availability of low-cost sensors, advances in computational resources, the maturity of mapping algorithms, and the need for accurate and on-demand geographic information system (GIS) data and digital maps. Many MMSs combine hybrid sensors to provide a more informative, robust, and stable solution by complementing each other. In this paper, we presented a comprehensive review of the modern MMSs by focusing on: (1) the types of sensors and platforms, discussing their capabilities and limitations and providing a comprehensive overview of recent MMS technologies available in the market; (2) highlighting the general workflow to process MMS data; (3) identifying different use cases of mobile mapping technology by reviewing some of the common applications; and (4) presenting a discussion on the benefits and challenges and sharing our views on potential research directions.

Retina-like Imaging and Its Applications: A Brief Review

The properties of the human eye retina, including space-variant resolution and gaze characters, provide many advantages for numerous applications that simultaneously require a large field of view, high resolution, and real-time performance. Therefore, retina-like mechanisms and sensors have received considerable attention in recent years. This paper provides a review of state-of-the-art retina-like imaging techniques and applications. First, we introduce the principle and implementing methods, including software and hardware, and describe the comparisons between them. Then, we present typical applications combined with retina-like imaging, including three-dimensional acquisition and reconstruction, target tracking, deep learning, and ghost imaging. Finally, the challenges and outlook are discussed to further study for practical use. The results are beneficial for better understanding retina-like imaging.

An Integrated Solution for 3D Heritage Modeling Based on Videogrammetry and V-SLAM Technology

This paper presents an approach for 3D reconstruction of heritage scenes using a videogrammetric-based device. The system, based on two video cameras with different characteristics, uses a combination of visual simultaneous localization and mapping (SLAM) and photogrammetry technologies. VSLAM, together with a series of filtering algorithms, is used for the optimal selection of images and to guarantee that the user does not lose tracking during data acquisition in real time. The different photogrammetrically adapted tools in this device and for this type of handheld capture are explained. An evaluation of the device is carried out, including comparisons with the Faro Focus X 330 laser scanner, through three case studies in which multiple aspects are analyzed. We demonstrate that the proposed videogrammetric system is 17 times faster in capturing data than the laser scanner and that the post-processing of the system is fully automatic, but takes more time than the laser scanner in post-processing. It can also be seen that the accuracies of both systems and the generated textures are very similar. Our evaluation demonstrates the possibilities of considering the proposed system as a new professional-quality measurement instrument.

Heuristic Analysis for In-Plane Non-Contact Calibration of Rulers Using Mask R-CNN

Determining an object measurement is a challenging task without having a well-defined reference. When a ruler is placed in the same plane of an object being measured it can serve as metric reference, thus a measurement system can be defined and calibrated to correlate actual dimensions with pixels contained in an image. This paper describes a system for non-contact object measurement by sensing and assessing the distinct spatial frequency of the graduations on a ruler. The approach presented leverages Deep Learning methods, specifically Mask Region proposal based Convolutional Neural Networks (R-CNN), for rulers’ recognition and segmentation, as well as several other computer vision (CV) methods such as adaptive thresholding and template matching. We developed a heuristic analytical method for calibrating an image by applying several filters to extract the spatial frequencies corresponding to the ticks on a given ruler. We propose an automated in-plane optical scaling calibration system for non-contact measurement.

High-Resolution Representation for Mobile Mapping Data in Curved Regular Grid Model

As mobile mapping systems become a mature technology, there are many applications for the process of the measured data. One interesting application is the use of driving simulators that can be used to analyze the data of tire vibration or vehicle simulations. In previous research, we presented our proposed method that can create a precise three-dimensional point cloud model of road surface regions and trajectory points. Our data sets were obtained by a vehicle-mounted mobile mapping system (MMS). The collected data were converted into point cloud data and color images. In this paper, we utilize the previous results as input data and present a solution that can generate an elevation grid for building an OpenCRG model. The OpenCRG project was originally developed to describe road surface elevation data, and also defined an open file format. As it can be difficult to generate a regular grid from point cloud directly, the road surface is first divided into straight lines, circular arcs, and and clothoids. Secondly, a non-regular grid which contains the elevation of road surface points is created for each road surface segment. Then, a regular grid is generated by accurately interpolating the elevation values from the non-regular grid. Finally, the curved regular grid (CRG) model files are created based on the above procedures, and can be visualized by OpenCRG tools. The experimental results on real-world data show that the proposed approach provided a very-high-resolution road surface elevation model.