scholarly journals Test Charts for Evaluating Imaging and Point Cloud Quality of Mobile Mapping Systems for Urban Street Space Acquisition

2021 ◽  
Vol 13 (2) ◽  
pp. 237
Author(s):  
Norbert Pfeifer ◽  
Johannes Falkner ◽  
Andreas Bayr ◽  
Lothar Eysn ◽  
Camillo Ressl
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Practical Method ◽  
Image Resolution ◽  
Test Method ◽  
Mobile Mapping ◽  
Point Distribution ◽  
Mobile Mapping Systems ◽  
Mapping Systems

Mobile mapping is in the process of becoming a routinely applied standard tool to support administration of cities. For ensuring the usability of the mobile mapping data it is necessary to have a practical method to evaluate the quality of different systems, which reaches beyond 3D accuracy of individual points. Such a method must be objective, easy to implement, and provide quantitative results to be used in tendering processes. We present such an approach which extracts quality figures for point density, point distribution, point cloud planarity, image resolution, and street sign legibility. In its practical application for the mobile mapping campaign of the City of Vienna (Austria) in 2020 the proposed test method proved to fulfill the above requirements. As an additional result, quality figures are reported for the panorama images and point clouds of three different mobile mapping systems.

scholarly journals MONITORING CAPABILITIES OF A MOBILE MAPPING SYSTEM BASED ON NAVIGATION QUALITIES

2016 ◽  
Vol XLI-B1 ◽  
pp. 625-631 ◽  
Author(s):  
H. Jing ◽  
N. Slatcher ◽  
X. Meng ◽  
G. Hunter
Keyword(s):  
Change Detection ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Mapping System ◽  
Short Period ◽  
Laser Scanners ◽  
Mobile Mapping System ◽  
Mobile Mapping Systems ◽  
Mapping Systems

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.

scholarly journals MONITORING CAPABILITIES OF A MOBILE MAPPING SYSTEM BASED ON NAVIGATION QUALITIES

2016 ◽  
Vol XLI-B1 ◽  
pp. 625-631
Author(s):  
H. Jing ◽  
N. Slatcher ◽  
X. Meng ◽  
G. Hunter
Keyword(s):  
Change Detection ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Mapping System ◽  
Short Period ◽  
Laser Scanners ◽  
Mobile Mapping System ◽  
Mobile Mapping Systems ◽  
Mapping Systems

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.

scholarly journals POINT-CLOUD COMPRESSION FOR VEHICLE-BASED MOBILE MAPPING SYSTEMS USING PORTABLE NETWORK GRAPHICS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 99-106 ◽  
Author(s):  
K. Kohira ◽  
H. Masuda
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Residential Areas ◽  
Compression Method ◽  
Mobile Mapping ◽  
Automatic Driving ◽  
Mobile Mapping Systems ◽  
Mapping Systems

A mobile mapping system is effective for capturing dense point-clouds of roads and roadside objects．Point-clouds of urban areas, residential areas, and arterial roads are useful for maintenance of infrastructure, map creation, and automatic driving. However, the data size of point-clouds measured in large areas is enormously large. A large storage capacity is required to store such point-clouds, and heavy loads will be taken on network if point-clouds are transferred through the network. Therefore, it is desirable to reduce data sizes of point-clouds without deterioration of quality. In this research, we propose a novel point-cloud compression method for vehicle-based mobile mapping systems. In our compression method, point-clouds are mapped onto 2D pixels using GPS time and the parameters of the laser scanner. Then, the images are encoded in the Portable Networking Graphics (PNG) format and compressed using the PNG algorithm. In our experiments, our method could efficiently compress point-clouds without deteriorating the quality.

scholarly journals Extraction of Guardrails from MMS Data Using Convolutional Neural Network

2021 ◽  
Vol 15 (3) ◽  
pp. 258-267
Author(s):  
Hiroki Matsumoto ◽  
◽  
Yuma Mori ◽  
Hiroshi Masuda
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Asset Management ◽  
Extraction Method ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Method Point ◽  
Mobile Mapping Systems ◽  
Capture Point ◽  
Mapping Systems

Mobile mapping systems can capture point clouds and digital images of roadside objects. Such data are useful for maintenance, asset management, and 3D map creation. In this paper, we discuss methods for extracting guardrails that separate roadways and walkways. Since there are various shape patterns for guardrails in Japan, flexible methods are required for extracting them. We propose a new extraction method based on point processing and a convolutional neural network (CNN). In our method, point clouds and images are segmented into small fragments, and their features are extracted using CNNs for images and point clouds. Then, features from images and point clouds are combined and investigated using whether they are guardrails or not. Based on our experiments, our method could extract guardrails from point clouds with a high success rate.

scholarly journals CLASSIFICATION OF RAILWAY ASSETS IN MOBILE MAPPING POINT CLOUDS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 219-225
Author(s):  
M. Corongiu ◽  
A. Masiero ◽  
G. Tucci
Keyword(s):  
Spatial Information ◽  
Point Clouds ◽  
Railway Track ◽  
Neural Net ◽  
Mobile Mapping ◽  
Geospatial Information ◽  
Railway System ◽  
Point Neighborhood ◽  
Mobile Mapping Systems ◽  
Mapping Systems

Abstract. Nowadays, mobile mapping systems are widely used to quickly collect reliable geospatial information of relatively large areas: thanks to such characteristics, the number of applications and fields exploiting their usage is continuously increasing. Among such possible applications, mobile mapping systems have been recently considered also by railway system managers to quickly produce and update a database of the geospatial features of such system, also called assets. Despite several vehicles, devices and acquisition methods can be considered for the data collection of the railway system, the predominant one is probably that based on the use of a mobile mapping system mounted on a train, which moves all along the railway tracks, enabling the 3D reproduction of the entire railway track area.Given the large amount of data collected by such mobile mapping, automatic procedures have to be used to speed up the process of extracting the spatial information of interest, i.e. assets positions and characteristics.This paper considers the problem of extracting such information for what concerns cantilever and portal masts, by exploiting a mixed approach. First, a set of candidate areas are extracted and pre-processed by considering certain of their geometric characteristics, mainly extracted by using eigenvalues of the covariance matrix of a point neighborhood. Then, a 3D modified Fisher vector-deep learning neural net is used to classify the candidates. Tests on such approach are conducted in two areas of the Italian railway system.

scholarly journals AUTOMATIC ROAD SIGN INVENTORY USING MOBILE MAPPING SYSTEMS

2016 ◽  
Vol XLI-B3 ◽  
pp. 717-723 ◽  
Author(s):  
M. Soilán ◽  
B. Riveiro ◽  
J. Martínez-Sánchez ◽  
P. Arias
Keyword(s):  
Point Cloud ◽  
Road Network ◽  
Spatial Information ◽  
Maintenance Planning ◽  
Mobile Mapping ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Mobile Mapping Systems ◽  
Mapping Systems

The periodic inspection of certain infrastructure features plays a key role for road network safety and preservation, and for developing optimal maintenance planning that minimize the life-cycle cost of the inspected features. Mobile Mapping Systems (MMS) use laser scanner technology in order to collect dense and precise three-dimensional point clouds that gather both geometric and radiometric information of the road network. Furthermore, time-stamped RGB imagery that is synchronized with the MMS trajectory is also available. In this paper a methodology for the automatic detection and classification of road signs from point cloud and imagery data provided by a LYNX Mobile Mapper System is presented. First, road signs are detected in the point cloud. Subsequently, the inventory is enriched with geometrical and contextual data such as orientation or distance to the trajectory. Finally, semantic content is given to the detected road signs. As point cloud resolution is insufficient, RGB imagery is used projecting the 3D points in the corresponding images and analysing the RGB data within the bounding box defined by the projected points. The methodology was tested in urban and road environments in Spain, obtaining global recall results greater than 95%, and F-score greater than 90%. In this way, inventory data is obtained in a fast, reliable manner, and it can be applied to improve the maintenance planning of the road network, or to feed a Spatial Information System (SIS), thus, road sign information can be available to be used in a Smart City context.

scholarly journals Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features

2019 ◽  
Vol 11 (16) ◽  
pp. 1955 ◽  
Author(s):  
Markus Hillemann ◽  
Martin Weinmann ◽  
Markus S. Mueller ◽  
Boris Jutzi
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Laser Scanner ◽  
The Self ◽  
Geometric Features ◽  
Mobile Mapping ◽  
Extrinsic Calibration ◽  
Self Calibration ◽  
Mobile Mapping Systems ◽  
Mapping Systems

Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the Rényi quadratic entropy to evaluate the suitability for the self-calibration. Furthermore, we perform tests of the self-calibration on synthetic and two different real datasets. The real datasets differ in terms of the environment, the scale and the utilized sensors. We show that the self-calibration is able to extrinsically calibrate Mobile Mapping Systems with different combinations of mapping and pose estimation sensors such as a 2D laser scanner to a Motion Capture System and a 3D laser scanner to a stereo camera and ORB-SLAM2. For the first dataset, the parameters estimated by our self-calibration lead to a more accurate point cloud than two comparative approaches. For the second dataset, which has been acquired via a vehicle-based mobile mapping, our self-calibration achieves comparable results to a manually refined reference calibration, while it is universally applicable and fully automated.

scholarly journals ACCURACY ASSESSMENT OF MOBILE MAPPING POINT CLOUDS USING THE EXISTING ENVIRONMENT AS TERRESTRIAL REFERENCE

2016 ◽  
Vol XLI-B1 ◽  
pp. 601-608 ◽  
Author(s):  
S. Hofmann ◽  
C. Brenner
Keyword(s):  
Point Cloud ◽  
Reference Data ◽  
Geodetic Network ◽  
Point Clouds ◽  
Test Field ◽  
Mobile Mapping ◽  
Total Station ◽  
Processed Products ◽  
Mapping Point

Mobile mapping data is widely used in various applications, what makes it especially important for data users to get a statistically verified quality statement on the geometric accuracy of the acquired point clouds or its processed products. The accuracy of point clouds can be divided into an absolute and a relative quality, where the absolute quality describes the position of the point cloud in a world coordinate system such as WGS84 or UTM, whereas the relative accuracy describes the accuracy within the point cloud itself. Furthermore, the quality of processed products such as segmented features depends on the global accuracy of the point cloud but mainly on the quality of the processing steps. Several data sources with different characteristics and quality can be thought of as potential reference data, such as cadastral maps, orthophoto, artificial control objects or terrestrial surveys using a total station. In this work a test field in a selected residential area was acquired as reference data in a terrestrial survey using a total station. In order to reach high accuracy the stationing of the total station was based on a newly made geodetic network with a local accuracy of less than 3 mm. The global position of the network was determined using a long time GNSS survey reaching an accuracy of 8 mm. Based on this geodetic network a 3D test field with facades and street profiles was measured with a total station, each point with a two-dimensional position and altitude. In addition, the surface of poles of street lights, traffic signs and trees was acquired using the scanning mode of the total station. <br><br> Comparing this reference data to the acquired mobile mapping point clouds of several measurement campaigns a detailed quality statement on the accuracy of the point cloud data is made. Additionally, the advantages and disadvantages of the described reference data source concerning availability, cost, accuracy and applicability are discussed.

scholarly journals Fully Automated Profile-based Calibration Strategy for Airborne and Terrestrial Mobile LiDAR Systems with Spinning Multi-beam Laser Units

2020 ◽  
Vol 12 (3) ◽  
pp. 401 ◽  
Author(s):  
Ravi ◽  
Habib
Keyword(s):  
Point Clouds ◽  
Mobile Systems ◽  
Full Potential ◽  
Mobile Mapping ◽  
Beam Laser ◽  
3D Point Clouds ◽  
Feature Based ◽  
True Values ◽  
Mobile Mapping Systems ◽  
Mapping Systems

LiDAR-based mobile mapping systems (MMS) are rapidly gaining popularity for a multitude of applications due to their ability to provide complete and accurate 3D point clouds for any and every scene of interest. However, an accurate calibration technique for such systems is needed in order to unleash their full potential. In this paper, we propose a fully automated profile-based strategy for the calibration of LiDAR-based MMS. The proposed technique is validated by comparing its accuracy against the expected point positioning accuracy for the point cloud based on the used sensors’ specifications. The proposed strategy was seen to reduce the misalignment between different tracks from approximately 2 to 3 m before calibration down to less than 2 cm after calibration for airborne as well as terrestrial mobile LiDAR mapping systems. In other words, the proposed calibration strategy can converge to correct estimates of mounting parameters, even in cases where the initial estimates are significantly different from the true values. Furthermore, the results from the proposed strategy are also verified by comparing them to those from an existing manually-assisted feature-based calibration strategy. The major contribution of the proposed strategy is its ability to conduct the calibration of airborne and wheel-based mobile systems without any requirement for specially designed targets or features in the surrounding environment. The above claims are validated using experimental results conducted for three different MMS – two airborne and one terrestrial – with one or more LiDAR unit.

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