TOWARDS DEVELOPMENT OF MOBILE MAPPING SYSTEMS

The mobile 3D mapping systems (MMS) and the integration with Geographical Information Systems (GIS) represent an innovative and efficient approach to modern topography. It is recommended and useful for infrastructure analysis and urban planning. This modern technology provides accurate, fast, and detailed data, facilitating well-informed decision-making and contributing to the development of sustainable infrastructure in the 21st century. The purpose of this research was to use a mobile mapping system for land surveying measurement of an urban area, independent of pre-established ground control points. On the other hand, the research is supposed to demonstrate the usefulness of kinematic trajectory, digital imaging, and laser scanning in acquiring, storing, and processing point cloud data. This paper shows an affordable method and a modern scanning instrument used for mobile mapping scanning, to create a georeferenced point cloud (PC). The obtained result has centimetric precision of georeferenced point cloud data, even if the average speed of the car was about 30km/h. The trajectory adjustment with static GNSS measurements increases the accuracy of ground control points (GCPs) up to ±5cm for horizontal positioning and vertical positioning. Although this study was carried out while the system was mounted on a car the result followed the national accuracy requirements for land surveying and cadastre fields. In other words, the mobile mapping system is a faster and cost-effective in time alternative to static laser scanning and it is important to continue to explore and adopt these technological solutions to face the future challenges in the field of urban development and infrastructure.

This paper discusses a comprehensive methodology for transforming a static LiDAR (Light Detection and Ranging) system into a mobile mapping system. The initial step involves integrating various sensors, such as GNSS (Global Navigation Satellite System), IMU (Inertial Measurement Unit), and odometry sensors, into the system’s architecture to accurately estimate the position and orientation of the LiDAR sensor at any given time. Specialized algorithms compensate for the vehicle’s motion, and procedural requirements must be followed to ensure safe operation. Hardware integration is critical, requiring proper calibration and validation. Data processing workflows, including algorithms and software tools, play a crucial role in mobile LiDAR mapping systems. The quality and accuracy of the data depend on various factors, and practical considerations such as cost and time are also important. The proposed methodology provides a clear and effective way to transform a terrestrial LiDAR sensor into a mobile mapping system, increasing its flexibility and usability for various applications. However, the detailed analysis, system validation, and data processing details fall outside this paper’s scope and will be discussed in future publications.

Mobile mapping systems are becoming increasingly popular as they can build 3D models of the environment rapidly by using a laser scanner that is integrated with a navigation system. 3D mobile mapping has been widely used for applications such as 3D city modelling and mapping of the scanned environments. However, accurate mapping relies on not only the scanner’s performance but also on the quality of the navigation results (accuracy and robustness) . This paper discusses the potentials of using 3D mobile mapping systems for landscape change detection, that is traditionally carried out by terrestrial laser scanners that can be accurately geo-referenced at a static location to produce highly accurate dense point clouds. Yet compared to conventional surveying using terrestrial laser scanners, several advantages of mobile mapping systems can be identified. A large area can be monitored in a relatively short period, which enables high repeat frequency monitoring without having to set-up dedicated stations. However, current mobile mapping applications are limited by the quality of navigation results, especially in different environments. The change detection ability of mobile mapping systems is therefore significantly affected by the quality of the navigation results. This paper presents some data collected for the purpose of monitoring from a mobile platform. The datasets are analysed to address current potentials and difficulties. The change detection results are also presented based on the collected dataset. Results indicate the potentials of change detection using a mobile mapping system and suggestions to enhance quality and robustness.

ABSTRACTMobile mapping systems (MMS) are widely used technology nowadays for spatial data collection of large scale projects like for city and highway mapping. The systems are mainly equipped with laser scanning sensors and/or imaging sensors mounted on a moving vehicle during the scene capture. Imaging sensors are normally cameras which either capture perspective or panoramic images covering the whole horizon of the vehicle. The orientation of the captured panoramic images is accurate to centimeters’ level because of the precise positioning and navigation systems equipped with these mapping systems. However, the positioning accuracy of mobile mapping systems can be degraded in city centers or urban canyons because of the satellite signal disturbances.In this article, we discuss the following objectives: (1) the possibility to use the mobile mapping images for cultural heritage documentation and as built surveying and how accurate the mapping can be; (2) the concept of using the mobile mapping images as a tool of georeferencing the crowdsource images; and (3) the efficiency of using the multi-temporal mobile mapping images for occluded free cultural heritage facade orthophotos. The mobile mapping systems of CycloMedia with two panoramic products of Cyclorama images (12 MP) and HD Cycloramas (100 MP) are used for the experimental tests in this research article.

Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data

Abstract. Mobile mapping systems are increasingly being used for the acquisition of 3D information of the environment. Although these systems are very efficient in data capturing compared to more traditional methods, the high cost of high-end accurate mobile mapping systems is a major drawback. In contrast, the much cheaper low-end mobile mapping systems are more frequently used for less accurate projects where visualization is more important. In general, the achievable accuracy level is the driving factor that differentiates low-end from high-end systems. To determine this value, the sensor quality, calibration and GNSS reception quality should be reliably evaluated.In this paper, we present a theoretical accuracy model of a mobile mapping system that takes into account variable GNSS accuracy. The predicted accuracy level of low-end and high-end mobile mapping systems is evaluated in a comprehensive accuracy analysis. The absolute accuracy of the system is determined in three datasets in which GNSS reception quality varies between optimal, good and poor. Additionally, the relative accuracy of both systems is checked by comparison of control distances. The presented approach allows for a more general and robust accuracy prediction of mobile mapping systems in different circumstances.

Mobile mapping is a method of acquisition of geo-referenced images that uses a moving platform in which sensors and other devices are set on board. Although the mobile mapping systems (MMS) were built to map roads they were extended to streets, railroads, waterways, environmental areas, and recently entered the buildings, concerning the land-based domain. More recently, MMS concepts were successfully implemented in unmanned aerial vehicles to fly safely. Mobile mapping technology requires measuring, processing and analysis of distinct data types: time, phase, range, force, angular rate, and distance, that provide position, velocity, and attitude (PVA) of one or more image sensors (cameras and LIDAR), which lead to geo-referenced images. The article brings a brief description, evolution, and technologic advances of the method and it also shows the main application branches. The mobile devices, the growing image sensor resolution, the low cost inertial sensors, and the computer performance of the 64-bit processors indicate that this methodology has potential for new applications. These new possibilities demand portability, mobility, and accessibility to sites where PVA have to be estimated by non satellite navigation methods. Image sensors, namely digital cameras and lidar devices, effectively materialize the process of visualization, measurement, recognition, and mapping 3D objects.

During the last few years, the acquisition of geometric information about objects by using a moving sensor platform has gained increasing popularity in the surveying community. A large number of companies offer vehicle-based mobile mapping systems, which usually contain a fast profile laser scanner, a high-precision inertial measurement unit, and geodetic global navigation satellite system (GNSS) receivers to directly georeference the laser scans and build a consistent point cloud of the object. In contrast, there is a growing number of companies offering small and lightweight unmanned aerial vehicles (UAVs), which automatically fly over the area of interest and create dense point clouds of the environment by using camera images and photogrammetric-processing software. In this case, the georeferencing is usually realized by ground control points. This contribution summarizes activities performed by the authors to bring both fields closer together by developing a small (11 × 10 × 5 cm) and lightweight (240-g) direct-georeferencing unit, which is able to provide accurate position (<5 cm) and orientation (<1°) information in real time. The authors describe the development of the sensor unit and the sensor-fusion algorithms, address the topics of calibration and accuracy evaluation, and provide an overview of different applications in which the unit has been already used. These include the high-resolution acquisition of crop-surface models by using UAV-based imagery, the direct georeferencing of an autonomously flying robot with a laser scanner and multiple cameras, and the generation of laser point clouds using a human-carried mobile mapping system.

GNSS/INS applications are being developed, especially for direct georeferencing in airborne photogrammetry. Achieving accurately georeferenced products from the integration of GNSS and INS requires removing systematic errors in the mobile mapping systems. The INS sensor's uncertainty is decreasing; therefore, the influence of the deflection of verticals (DOV, the angle between the plumb line and normal to the ellipsoid) should be considered in the direct georeferencing. Otherwise, an error is imposed for calculating the exterior orientation parameters of the aerial images and aerial laser scanning. This study determines the DOV using the EGM2008 model and gravity data in Sweden. The impact of the DOVs on horizontal and vertical coordinates, considering different flight altitudes and camera field of view, is assessed. The results confirm that the calculated DOV components using the EGM2008 model are sufficiently accurate for aerial mapping system purposes except for mountainous areas because the topographic signal is not modelled correctly.

In the last two decades, the demand for geo-spatial data has tremendously increased. Mobile mapping Systems (MMS) are considered the main source of data for mapping and Geographic Information Systems (GIS) applications. MMS, which integrates navigation systems (such as Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS)) and imaging sensors (such as cameras and laser scanners), have been an emerging trend in mapping applications because they allow the implementation of the mapping concepts from moving vehicles (e.g. cars, airplanes and trains). The main strength of MMS which makes it desirable for different mapping applications is their ability to directly georeference their mapping sensors, which means knowing their position and orientation relative to a mapping co-ordinate frame. Once geo-referenced, the mapping sensor can be used to determine the positions of points external to the platform in the same mapping co-ordinate frame. Nevertheless, the major drawback of current MMS systems is their high price due to the type of the used platform, navigation and mapping technologies. Today's smartphones are getting more sophisticated and smarter and therefore they will narrow the gap between computers and mobile phones. Current generation of smartphones are equipped with GNSS receivers, micro-electro mechanical systems (MEMS) based navigation sensors (such as gyroscopes, accelerometers, magnetometers, and barometers) and high resolution digital cameras. As a result, smartphones are now getting quite popular for data collection projects including point data mapping tasks and it is not far before we see smartphones are used as mobile mappers. This paper will present a new and innovative application of a portable MMS based on smartphones that overcome the high-cost drawback of the current land-based MMS with reasonable accuracy.

Abstract. LIDAR sensors are widely used in mobile mapping systems. The mobile mapping platforms allow to have fast acquisition in cities for example, which would take much longer with static mapping systems. The LIDAR sensors provide reliable and precise 3D information, which can be used in various applications: mapping of the environment; localization of objects; detection of changes. Also, with the recent developments, multi-beam LIDAR sensors have appeared, and are able to provide a high amount of data with a high level of detail. A mono-beam LIDAR sensor mounted on a mobile platform will have an extrinsic calibration to be done, so the data acquired and registered in the sensor reference frame can be represented in the body reference frame, modeling the mobile system. For a multibeam LIDAR sensor, we can separate its calibration into two distinct parts: on one hand, we have an extrinsic calibration, in common with mono-beam LIDAR sensors, which gives the transformation between the sensor cartesian reference frame and the body reference frame. On the other hand, there is an intrinsic calibration, which gives the relations between the beams of the multi-beam sensor. This calibration depends on a model given by the constructor, but the model can be non optimal, which would bring errors and noise into the acquired point clouds. In the litterature, some optimizations of the calibration parameters are proposed, but need a specific routine or environment, which can be constraining and time-consuming. In this article, we present an automatic method for improving the intrinsic calibration of a multi-beam LIDAR sensor, the Velodyne HDL-32E. The proposed approach does not need any calibration target, and only uses information from the acquired point clouds, which makes it simple and fast to use. Also, a corrected model for the Velodyne sensor is proposed. An energy function which penalizes points far from local planar surfaces is used to optimize the different proposed parameters for the corrected model, and we are able to give a confidence value for the calibration parameters found. Optimization results on both synthetic and real data are presented.

A Mobile Mapping System (MMS) is composed by a set of sensors placed on a mobile platform whose main objective is to obtain attitude and position over time of a referential in the platform together with the image of the objects surrounding the platform. The acquired data allows to obtain georeferenced positions of object points captured by the object sensors. This paper describes a methodology to automatically extract accurate Digital Terrain Models (DTM) of sand beach areas using a Terrestrial Mobile Mapping System (TMMS). The main goal of the presented TMMS is to obtain accurate 3D models of sand beaches. A very important consequence of this will be the abbility to track spatial and temporal changes in coastal geomorphology. The presented methodology has several advantages over more classical approaches: the overall cost, flexibility for regular surveys or at the most convenient epoch, such as after storms. Besides one of the major constraints in Mobile Mapping, that is poor GNSS (Global Navigation Satellite Systems) observation conditions, is usually not present in these areas. The TMMS uses two progressive colour video cameras and can work with any type of direct georeferencing system, which obvoiusly has impact in the final accuracy of the derived DTM. For the results herein reported, a dual frequency GNSS receiver and a low grade type of IMU (Inertial Measurement System) were used. The moving platform where the system was installed is a moto quad. The developed methodology for the DTM generation relies on an automatic choice of terrain points whose three-dimensional coordinates are calculated using a presented image matching technique. A cloud of points obtained in a nearly regular grid pattern is the base for the DTM. The proposed methodology was applied on a sector of the Vila Nova de Gaia Coast, in northern Portugal, and the results presented here show that it is possible with this methodology to produce DTMs with a precision of a few centimetres.

Accuracy assessment of integrated GNSS measurements with LIDAR mobile mapping data in urban environments

Abstract. The last two decades have witnessed a huge growth in the demand for geo-spatial data. This demand has encouraged researchers around the world to develop new algorithms and design new mapping systems in order to obtain reliable sources for geo-spatial data. Mobile Mapping Systems (MMS) are one of the main sources for mapping and Geographic Information Systems (GIS) data. MMS integrate various remote sensing sensors, such as cameras and LiDAR, along with navigation sensors to provide the 3D coordinates of points of interest from moving platform (e.g. cars, air planes, etc.). Although MMS can provide accurate mapping solution for different GIS applications, the cost of these systems is not affordable for many users and only large scale companies and institutions can benefits from MMS systems. The main objective of this paper is to propose a new low cost MMS with reasonable accuracy using the available sensors in smartphones and its video camera. Using the smartphone video camera, instead of capturing individual images, makes the system easier to be used by non-professional users since the system will automatically extract the highly overlapping frames out of the video without the user intervention. Results of the proposed system are presented which demonstrate the effect of the number of the used images in mapping solution. In addition, the accuracy of the mapping results obtained from capturing a video is compared to the same results obtained from using separate captured images instead of video.

An emerging solution to the problems faced in modern data collection campaigns is the integration of various navigation and remote sensing technologies together on a common land-based platform. These land-based Mobile Mapping Systems (MMS) are capable of providing fast, efficient, cost-effective, and complete data collection. Their development has been motivated by a desire to overcome the problems with alternative methods of spatial data collection. These alternative methods include point-wise GPS and traditional terrestrial surveying — which are ill suited for rapid or dense data collection — and aerial photogrammetric surveys — which do not provide complete coverage. Land-based MMS share none of these disadvantages, while still being capable of providing similar object space accuracies.This paper provides a brief history of land-based MMS, including a summary of nearly all systems developed up to now. It then details the development of the geo-referencing formulas used by MMS and shows how such formulas can be used to determine the accuracies of points measured by the system. The paper concludes with a closer examination of two land-based mobile mapping systems — one vanbased, the other person-based. Both systems demonstrate that MMS are able to achieve absolute object-space accuracies between 10–20 em.