Mobile Mapping Research Articles

Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system

Abstract Kinematic multi-sensor systems (MSS) are widely used for various applications, like mobile mapping or for autonomous systems. Depending on the application, insufficient knowledge of a system, like wrong assumptions about the accuracy of calibrations, might lead to inaccurate maps for mapping tasks or it might endanger humans in the context of autonomous driving. Uncertainty modeling can help to gain knowledge about the data captured by a system. Usually, uncertainty estimations for MSSs are done as backward modeling based on a comparison to reference datasets. In this paper, a forward modeling approach for the uncertainty modeling of a LiDAR-based kinematic MSS is chosen to estimate the uncertainty of an acquired point cloud. The MSS consists of a Leica Absolute Tracker and a platform with a 6-DoF sensor and Velodyne VLP-16 LiDAR. Results of multiple calibrations are used as the source for the uncertainty information for a Monte Carlo (MC) variance propagation of the point uncertainties. The deviations of the acquired point clouds in comparison to a ground truth can be decreased by an ensemble referencing process using the MC samples. Furthermore, the predicted uncertainties for the point clouds are well representing the actual deviations for reference panels closer to the system. Panels farther away indicate remaining distance depending effects.

OBTAINING APPROXIMATIONS FOR RANGE-BASED FREE-NETWORK ADJUSTMENT

Abstract. Airborne and ground mobile mapping platforms are increasingly used in group formations to increase productivity or complement each other in terms of improving observation capacity and efficiency in the surveyed area. Similarly, the navigation of assisted and autonomous vehicles presents a similar problem of sharing a small place where, for example, accurate relative positioning is essential to avoid collisions. Estimating platform relative positions from inter-platform range measurements is important in these applications, as they can provide valuable information to improve individual platform navigation or can potentially detect anomalies in these solutions that could be caused by unintentional or intentional disturbances. Free-network adjustment based on ranges forms the baseline solution to obtain relative positions. The challenge is to provide adequate platform position approximations for the least squares adjustment to achieve both quick convergence and fast execution time. Here we propose a preprocessing method that creates suitable approximations based on range values.

Metrological issues in the use of mobile mapping systems for planning emergency response

The paper intends to investigate some metrological aspects concerning data acquisition in determining the slope of escape routes in existing building, in order to provide this information as input for subsequent evacuation planning algorithms. The analysis is based on the point cloud acquired by a laser scanner and deepens different aspect that can impact on the slope uncertainty, linked to the choice of the algorithm for determining the slope itself, the values of the different parameters to set, and the different possible study strategies of the floor surface. The analysis will be conducted by using a commercial software, with the aim, in a future work, of investigating other kinds of algorithms and approaches, but also non-commercial data processing methods that may provide further useful information.

Cognitive issues of mobile map design and use: A collaborative research agenda

Cognitive issues of mobile map design and use: A collaborative research agenda

Leveraging map use context for advancing cartography in the 21st century

Abstract. Mobile maps are an integral part of our daily routines, serving a variety of purposes in different environments. Designing maps for different use situations is essential for a user-centered and context-aware approach. Previous research has explored map use context and context-aware mobile maps from interdisciplinary perspectives. This paper aims to consolidate and unify existing research on context in cartography and related fields, identify current challenges, and propose ways to advance context-awareness for designing mobile maps. We present a map use context taxonomy that provides an overview of context elements, possible context-sensing methods, and corresponding application fields. We invite the cartographic community to expand on our proposed context taxonomy and explore the extensive field of context acquisition methods, applications, and related literature for advancing research on map use context.

Unscented Kalman filter for a low-cost GNSS/IMU-based mobile mapping application under demanding conditions

For the last two decades, low-cost Global Navigation Satellite System (GNSS) receivers have been used in various applications. These receivers are mini-size, less expensive than geodetic-grade receivers, and in high demand. Irrespective of these outstanding features, low-cost GNSS receivers are potentially poorer hardwares with internal signal processing, resulting in lower quality. They typically come with low-cost GNSS antenna that has lower performance than their counterparts, particularly for multipath mitigation. Therefore, this research evaluated the low-cost GNSS device performance using a high-rate kinematic survey. For this purpose, these receivers were assembled with an Inertial Measurement Unit (IMU) sensor, which actively transmited data on acceleration and orientation rate during the observation. The position and navigation parameter data were obtained from the IMU readings, even without GNSS signals via the U-blox F9R GNSS/IMU device mounted on a vehicle. This research was conducted in an area with demanding conditions, such as an open sky area, an urban environment, and a shopping mall basement, to examine the device's performance. The data were processed by two approaches: the Single Point Positioning-IMU (SPP/IMU) and the Differential GNSS-IMU (DGNSS/IMU). The Unscented Kalman Filter (UKF) was selected as a filtering algorithm due to its excellent performance in handling nonlinear system models. The result showed that integrating GNSS/IMU in SPP processing mode could increase the accuracy in eastward and northward components up to 68.28% and 66.64%. Integration of DGNSS/IMU increased the accuracy in eastward and northward components to 93.02% and 93.03% compared to the positioning of standalone GNSS. In addition, the positioning accuracy can be improved by reducing the IMU noise using low-pass and high-pass filters. This application could still not gain the expected position accuracy under signal outage conditions.

Detecting School Zones on Florida’s Public Roadways Using Aerial Images and Artificial Intelligence (AI2)

Collecting up-to-date roadway geometry data is essential for transportation agencies so they can undertake planning, maintenance, design, and rehabilitation of roadways. Collection methods can be categorized into two distinct groups: land-based methods (e.g., field inventories, mobile mapping, and image logging); and aerial-based methods (e.g., satellite imagery, drones, and laser scanning). Because using land-based methods for thousands of miles of highways is tedious, costly, and risky for crew members, there is a need to develop better methodologies to collect these data faster, and more safely and cheaply. The increasing availability of high-resolution images and recent advances in computer vision and object detection techniques have enabled the automated extraction of roadway geometry features. This novel study proposes a computer vision-based methodology to detect school zone pavement markings from high-resolution aerial images and determine school zones on Florida’s public roadways. This is critical information for transportation agencies, and they use it for a variety of different purposes: identifying those markings that are old and invisible; comparing the school zone locations with other geometric features such as crosswalks; and analyzing crashes that occur around the zones. Compared with the ground truth data obtained for Leon County, Florida, 94% accuracy was observed at the 90% confidence level. The model was then used to detect school zones in Orange County, Florida, and approximately 500 school zone markings were identified automatically. The road geometry data extracted can be integrated with crash and traffic data to advise policymakers and roadway users.

A mobile mapping solution for VRU Infrastructure monitoring via low-cost LiDAR-sensors

ABSTRACT LiDAR scanning is a widely-used method for accurately assessing roads and vehicular infrastructure. However, it can be costly and often overlooks infrastructure important for Vulnerable Road Users (VRU) like pedestrians and cyclists. Advances in electronics now permit high-performance LiDAR sensors made of standard semiconductor materials to be integrated into personal devices like smartphones and tablets. This study introduces a new classification category of handheld LiDAR systems for the purpose of VRU infrastructure mapping on a smaller scale. Qualitative experiments using a handheld LiDAR system were conducted to achieve low-cost mobile mapping of VRU infrastructure. The 3D Scanner App facilitated detailed and qualitative point cloud generation at low speeds. Moreover, it demonstrated precise detection of perpendicular potholes and bumps on roads. A quantitative analysis based on several infrastructure damages was performed, revealing mean average percentage errors of 3.53%, 4.43%, and 10.57% in the x-, y-, and z-directions, respectively. While the accuracy in the x- and y-directions is acceptable, the higher z-direction error indicates overestimation of infrastructural damage depth. In conclusion, the sensor's capabilities enable cost-effective mobile mapping solutions for VRU infrastructure monitoring, such as crowd-sourcing for urban data mining and urban digital twin interfaces, significantly reducing expenses compared to traditional sensors.

Gamification for road asset inspection from Mobile Mapping System data

ABSTRACT Gamification techniques have been proven effective in various fields such as education and industry. In this paper, we introduce a novel approach that applies gamification techniques to the identification of road assets in Mobile Laser Scanning (MLS) data. Our method utilises three gamification techniques: avatar (vehicle), point cloud segmentation into levels, and scoring. We implemented these techniques in Unreal Engine and evaluated their performance using three real-world case studies. We also compared two ways of point cloud visualisation: mesh-based and point-based. Our results demonstrate that our gamification approach improves the handling and visualisation of point clouds when compared to other free software such as Cloud Compare. Specifically, the point-based visualisation method provides a more accurate representation of the road environment and the input point cloud and is easier to import into Unreal Engine. However, this method requires more computational resources for visualisation. On the other hand, level segmentation ensures a constant frame rate of 60 frames per second. Furthermore, our gamification approach enhances the experience of road asset identification, making it more enjoyable for the user. However, we acknowledge that the quality of the point cloud remains the primary factor affecting the accuracy of asset identification, regardless of the software used. Overall, our proposed gamification approach offers a promising solution for improving the identification of road assets in MLS data and has the potential to be applied to other fields beyond road asset identification.

Fusion of aerial, MMS and backpack images and point clouds for optimized 3D mapping in urban areas

Photorealistic 3D models are important data sources for digital twin cities and smart city applications. These models are usually generated from data collected by aerial or ground-based platforms (e.g., mobile mapping systems (MMSs) and backpack systems) separately. Aerial and ground-based platforms capture data from overhead and ground surfaces, respectively, offering complementary information for better 3D mapping in urban areas. Particularly, backpack mapping systems have gained popularity for 3D mapping in urban areas in recent years, as they offer more flexibility to reach regions (e.g., narrow alleys and pedestrian routes) inaccessible by vehicle-based MMSs. However, integration of aerial and ground data for 3D mapping suffers from difficulties such as tie-point matching among images from different platforms with large differences in perspective, coverage, and scale. Optimal fusion of the results from different platforms is also challenging. Therefore, this paper presents a novel method for the fusion of aerial, MMS, and backpack images and point clouds for optimized 3D mapping in urban areas. A geometric-aware model for feature matching is developed based on the SuperGlue algorithm to obtain sufficient tie-points between aerial and ground images, which facilitates the integrated bundle adjustment of images to reduce their geometric inconsistencies and the subsequent dense image matching to generate 3D point clouds from different image sources. After that, a graph-based method considering both geometric and texture traits is developed for the optimal fusion of point clouds from different sources to generate 3D mesh models of better quality. Experiments conducted on a challenging dataset in Hong Kong demonstrated that the geometric-aware model could obtain sufficient accurately matched tie-points among the aerial, MMS, and backpack images, which enabled the integrated bundle adjustment of the three image datasets to generate properly aligned point clouds. Compared with the results obtained from state-of-the-art commercial software, the 3D mesh models generated from the proposed point cloud fusion method exhibited better quality in terms of completeness, consistency, and level of detail.

PVL-Cartographer: Panoramic Vision-Aided LiDAR Cartographer-Based SLAM for Maverick Mobile Mapping System

The Mobile Mapping System (MMS) plays a crucial role in generating accurate 3D maps for a wide range of applications. However, traditional MMS that utilizes tilted LiDAR (light detection and ranging) faces limitations in capturing comprehensive environmental data. We propose the “PVL-Cartographer” SLAM (Simultaneous Localization And Mapping) approach for MMS to address these limitations. This proposed system incorporates multiple sensors to yield dependable and precise mapping and localization. It consists of two subsystems: early fusion and intermediate fusion. In early fusion, range maps are created from LiDAR points within a panoramic image space, simplifying the integration of visual features. The SLAM system accommodates both visual features with and without augmented ranges. In intermediate fusion, camera and LiDAR nodes are merged using a pose graph, with constraints between nodes derived from IMU (Inertial Measurement Unit) data. Comprehensive testing in challenging outdoor settings demonstrates that the proposed SLAM system can generate trustworthy outcomes even in feature-scarce environments. Ultimately, our suggested PVL-Cartographer system effectively and accurately addresses the MMS localization and mapping challenge.

Lane Information Extraction for High Definition Maps Using Crowdsourced Data

Lane information plays an important role in high-definition (HD) maps because it provides invaluable road information for autonomous vehicles. The most widely used lane extraction method for HD maps is based on mobile mapping systems, and it is prohibitively costly and time-consuming. In this study, we propose a novel approach for lane information extraction based on crowdsourcing vehicles equipped with monocular cameras and global navigation satellite system devices for recording road images and position data. First, we propose a lane mask propagation network to detect the lane markings in images, which are then projected from a perspective space into a three-dimensional space in accordance with the position data. Second, we propose a data management method to store lane information in the cloud data center. An improved density-based spatial clustering of applications with noise clustering algorithm and a gradual fitting algorithm are used to remove the outliers and improve the lane data accuracy. The proposed method is quantitatively evaluated against a real-world HD map produced by a mobile mapping vehicle. The experimental results show that more than 80% of the extracted lane markings meet the accuracy requirements of HD maps. In conclusion, our method can be used as a low-cost and efficient approach for updating the lane information in HD maps for autonomous vehicles.

Artificial Intelligence Based LOS Determination for the Cyclists-Pedestrians Mixed Road Using Mobile Mapping System

Artificial Intelligence Based LOS Determination for the Cyclists-Pedestrians Mixed Road Using Mobile Mapping System

PRELIMINARY SURVEY OF HISTORIC BUILDINGS WITH WEARABLE MOBILE MAPPING SYSTEMS AND UAV PHOTOGRAMMETRY

Abstract. In cultural heritage, three-dimensional documentation of historic buildings is fundamental for conservation and valorisation projects. In recent years, the consolidated tools and methods: Terrestrial Laser Scanning (TLS) and close-range photogrammetry, have been joined by portable Mobile Mapping Systems (MMSs), which can offer significant advantages in terms of speed of survey operations at the price of reduced accuracy. The reduction of survey times and, therefore, costs makes the application of MMS techniques ideal for the preliminary stages of analysis of historical artifacts, when a rapid survey is indispensable for estimating the costs of conservation interventions.In this paper, we present a methodology for the expeditious survey of historic buildings and the surrounding urban fabric that is based on the use of an MMS and an Unmanned Aerial Vehicle (UAV). The MMS is the Gexcel Heron MS Twin color. It was used to survey two architecture of interest and the urban context surrounding them from the ground level. The UAV is the DJI Mini 2, used to integrate the terrestrial survey by acquiring the buildings' roofs. The case study presented in the paper is the survey of San Clemente and San Zeno al Foro churches, two historic churches in the city centre of Brescia (Italy).The result are a complete point cloud of the two buildings and a metric virtual tour of all spaces. These results were made available to the architects through the Cintoo web platform to plan future activities.

EVALUATION OF A SLAM-BASED POINT CLOUD FOR DEFLECTION ANALYSIS IN HISTORIC TIMBER FLOORS

Abstract. This paper aims at evaluating the possibility of using wearable mobile mapping solutions as a tool for detecting deflections in timber floors. These construction systems are prone to present this type of damage due to the mechanical properties of the wood (relatively low flexural stiffness and creep behaviour). During this study we have evaluated the chance of introducing an additional stage to the general workflow. This stage is devoted to reduce the noise of the 3D point cloud by using the Statistical Outlier Removal filter in combination with a noise-reduction filter such as the Anisotropic filter, the PointCleannet or the Scored-based denoised networks (Deep Learning methods). According with our results, the use of this strategies improves the quality of the 3D point cloud form a qualitative and quantitative point of view. However, these improvements seem to be not sufficient for using this product as a universal source of information for deflection analysis. In this sense, and according with the sensor and study case exploited, this type of point clouds could be used in floors with 5-8-meter length and a relative deflection of about L/200 or higher.

UNDERGROUND HERITAGE DOCUMENTATION: THE CASE STUDY OF GROTTA ZINZULUSA IN CASTRO (LECCE-ITALY)

Abstract. 3D documentation of the Underground Built Heritage (UBH) is always fundamental for knowledge, management, conservation, and enhancement of cultural heritage, but the challenges involved in documenting this kind of site are many and various, With the aim of providing an accurate and reliable representation of the surveyed area without employing time-consuming processes, Mobile Mapping Systems (MMSs) based on SLAM (Simultaneous Localization And Mapping) technology have now the potential to overcome these challenges, proving to be a useful alternative to traditional surveying techniques. The underground site of Grotta Zinzulusa in Castro (LE), Italy, is an anchialine cave system difficult to access and document due to its narrow passages, large rooms, and poor illumination. Within this case study, the effectiveness of using an MMS approach, employing the STONEX® X120GO SLAM Laser Scanner system, to document the cave system and integrates the data with those obtained from other traditional geomatics techniques, such as UAS (Uncrewed Aerial System) and TLS (Terrestrial Laser Scanning) surveys, has been proved. The study aims to define the best practices and operational methodologies for enhancing the speediness, usability, and cost-effectiveness of using MMSs in underground settings for 3D documentation of heritage sites to provide metrically correct and accurate products for researchers, scientists, and the wider public.

LESSONS LEARNT FROM THE HIGH RESOLUTION UAS PHOTOGRAMMETRIC SURVEY OF A HISTORIC URBAN AREA: UNESCO SITE OF SABBIONETA

Abstract. In view of the increasing development of the smart city concept and the generation of 3d city models, it becomes essential to have an up-to-date, high-detail 3D survey of urban areas. There are several technologies that enable the development of a geometric survey of urban areas, including the use of aerial laser scanning, remote sensing, mobile mapping systems, and Unmanned Aerial Sistems (UAS) photogrammetry. Of these, the last mentioned, when developed with high-resolution cameras and flight plans with appropriate elevations, allows point clouds to be obtained at a high level of detail and orthophotos with great resolution. This technique may be the preferable choice when the object of the survey is a historic urban area, which has some special features, which might make surveying difficult with other surveying techniques. This paper presents the survey with UAS photogrammetry of a historic urban area: the city of Sabbioneta, in northern Italy. The UAS flight planning is discussed in details, specifically referring to european UAS flight regulations. The survey was developed with DJI Matrice 300 RTK, equipped with a flight terminator and parachute, and coupled with a high precision GNSS Mobile Station DJI D-RTK 2. Ground control points and check points were measured with GNSS receiver Leica GS18. Images were processed with Agisoft Metashape following a photogrammetric workflow. The resulting orthophoto has a pixel size of 1 cm. The obtained dense point cloud is suitable for future use for its segmentation by testing existing machine learning and deep learning methods and for future urban analysis.

LEICA BLK2GO POINT CLOUD CLASSIFICATION WITH MACHINE LEARNING ALGORITHMS: THE CASE STUDY OF SANT’EUSEBIO’S CRYPT IN PAVIA (ITALY)

Abstract. Valorisation of cultural heritage is a priority of international community, and the creation of 3D models is considered almost a mandatory requirement to any conservation activity. Geomatics has given its contribution to this purpose offering its competences and experiences in surveying. Methods and tools have progressively improved offering more realistic, accurate, and reliable products; among the most interesting systems currently available, there is the handheld mobile mapping Leica BLK2GO technology. The characteristics of this system are particularly useful when traditional methods are unfeasible due to accessibility problem, narrowness spaces and time constraints. Nevertheless, the system, as any new technology, needs to be tested in order to verify its capability to describe the world in a correct and reliable way; moreover, it is interesting to understand if established data classification procedures are still effective for SLAM (Simultaneous Localization and Mapping) data. The paper is framed in this context and illustrates the survey of an ancient crypt, located in Pavia (Italy), that is object of a preservation project. The characteristics of the monument and the acquisition strategies are described. For data classification three different Machine Learning approaches are test: Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF). Seven architectural elements are considered: pavement, columns, half pilasters with structural function, walls, capitals, arches, and vaults. The analysis shows as Leica BLK2GO data owns all the characteristics feasible to produce useful point cloud; data classification performs well (with the exceptions of SVM) with higher accuracy, of about 90%, reached using RF.

MOBILE MAPPING SOLUTIONS FOR THE UPDATE AND MANAGEMENT OF TRAFFIC SIGNS IN A ROAD CADASTRE FREE OPEN-SOURCE GIS ARCHITECTURE

Abstract. The adoption of open-source mobile mapping applications in public administration has risen over the last decade due to their interdisciplinary vocation and flexibility in adapting to existing Geographic Information System (GIS) software architectures. This facilitates complex procedures of data collection and management required for transportation and environmental models, emergency management, and maintenance operations. The Ministry of Infrastructure and Transport in Italy requires road-owning agencies to build and maintain a mapping inventory of their road networks, including georeferenced information about streets and ancillary elements, such as traffic signs. Innovative and integrated street-level approaches for the rapid mapping of road entities using open-source mobile mapping tools represent a valuable low-cost solution for the periodical update of road entities inventory. The adoption of these tools allows public administrators to easily consult the road inventory even outside the office, conducting in situ validation and quality evaluation. This work presents a case study focused on the update and management of traffic sign entities of the Road Cadastre of the Province of Piacenza (Italy) using Qfield and Open Data Kit (ODK) Collect as alternatives to the previous traditional survey method that consisted in the use of field papers. A comparison between the adoption of the two mobile apps is conducted, identifying benefits and limitations in terms of both data accuracy and usability. Validation scripts, project and form structure were developed with the perspective of making the entire workflow as reproducible and transparent as possible, sharing details in a dedicated GitHub repository.

MEASURING ROAD STRUCTURES USING A MOBILE MAPPING SYSTEM

Abstract. The purpose of this thesis is to provide a detailed description of the Riegl VMX-2HA mobile mapping systém (MMS), its functions, composition and the data acquisition and processing procedure. In addition, this work presents a comparative analysis of the systém‘s height accuracy in relation to measurements made with the Leica NOVA MS50 multi-station. Using an experimental method, this study also shows that the order in which the described steps are performed can significantly affect the accuracy of the collected data. The results show that the use of the MMS in combination with appropriate data processing techniques can result in highly accurate data that can be used for a wide range of applications in the field of transportation infrastructure management and development. By providing a deeper understanding of the functions and capabilities of the MMS, this thesis describes valuable knowledge for professionals in the field seeking to optimize their data collection and analysis methods.