scholarly journals Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

2017 ◽  
Vol 9 (10) ◽  
pp. 975 ◽  
Author(s):  
Mahdi Javanmardi ◽  
Ehsan Javanmardi ◽  
Yanlei Gu ◽  
Shunsuke Kamijo
Keyword(s):  
Aerial Imagery ◽  
High Definition ◽  
Mobile Mapping ◽  
Feature Based ◽  
Urban Mapping ◽  
Mobile Mapping Systems ◽  
Mapping Systems

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.

scholarly journals Mobile Mapping Technologies

2021 ◽  
pp. 439-465
Author(s):  
Kai Wei Chiang ◽  
Guang-Je Tsai ◽  
Jhih Cing Zeng
Keyword(s):  
Sensor Fusion ◽  
Disaster Response ◽  
Autonomous Systems ◽  
High Definition ◽  
Mobile Mapping ◽  
Potential Applications ◽  
Historic Development ◽  
Mobile Mapping Systems ◽  
Indoor And Outdoor ◽  
Mapping Systems

AbstractThis chapter introduces the historic development as well as the latest progress of mobile mapping systems. First, mobile mapping technologies, including the introduction of positioning and mapping sensors, and how they can be integrated together, are briefly reviewed. Then the development of land-based, aerial, marine, and mobile portable mapping platforms is presented. The latest progress in mobile-mapping technologies is further discussed, along with sensor fusion schemes, seamless indoor and outdoor mapping strategies, and disaster response applications. In addition, this chapter explores future and potential applications, such as high-definition (HD) maps and autonomous mapping with autonomous systems.

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 APPLICATION OF MLS DATA TO THE ASSESSMENT OF SAFETY-RELATED FEATURES IN THE SURROUNDING AREA OF AUTOMATICALLY DETECTED PEDESTRIAN CROSSINGS

2018 ◽  
Vol XLII-2 ◽  
pp. 1067-1074 ◽  
Author(s):  
M. Soilán ◽  
B. Riveiro ◽  
A. Sánchez-Rodríguez ◽  
L. M. González-deSantos
Keyword(s):  
Laser Scanning ◽  
Geographical Information ◽  
Mobile Mapping ◽  
Cloud Data ◽  
Traffic Lights ◽  
The Road ◽  
Processing Module ◽  
Pedestrian Crossing ◽  
Mobile Mapping Systems ◽  
Mapping Systems

During the last few years, there has been a huge methodological development regarding the automatic processing of 3D point cloud data acquired by both terrestrial and aerial mobile mapping systems, motivated by the improvement of surveying technologies and hardware performance. This paper presents a methodology that, in a first place, extracts geometric and semantic information regarding the road markings within the surveyed area from Mobile Laser Scanning (MLS) data, and then employs it to isolate street areas where pedestrian crossings are found and, therefore, pedestrians are more likely to cross the road. Then, different safety-related features can be extracted in order to offer information about the adequacy of the pedestrian crossing regarding its safety, which can be displayed in a Geographical Information System (GIS) layer. These features are defined in four different processing modules: Accessibility analysis, traffic lights classification, traffic signs classification, and visibility analysis. The validation of the proposed methodology has been carried out in two different cities in the northwest of Spain, obtaining both quantitative and qualitative results for pedestrian crossing classification and for each processing module of the safety assessment on pedestrian crossing environments.

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.

High Definition Corridor Mapping From Images Sequences

2020 ◽  
Vol 9 (1) ◽  
pp. 22-35
Author(s):  
Mert Gurturk ◽  
Yalçın Yılmaz ◽  
Baris Suleymanoglu ◽  
Arzu Soycan ◽  
Metin Soycan
Keyword(s):  
Autonomous Vehicles ◽  
Low Cost ◽  
Road Surface ◽  
High Definition ◽  
Intelligence Studies ◽  
Technological Developments ◽  
Vehicle Technology ◽  
Imaging Sensors ◽  
Mobile Mapping Systems ◽  
Mapping Systems

The density, high accuracy, and rapid collection of geographical data for road surface and surrounding objects and the extraction of meaningful information from these data increases its importance in line with technological developments. Artificial intelligence studies and developments in cloud technology have affected the automotive industry as well as every sector and have enabled the development of driverless vehicle technology. In order to safely drive with autonomous vehicles, high definition maps that contain detailed information for road surface and its surrounding objects with high precision at centimeter-level must be used. In this context, in recent years, the development of mobile mapping systems (MMS) consisting of low-cost sensors and the development of algorithms for the evaluation of the data obtained from these systems have become increasingly popular. In this study, it was investigated whether HD maps can be obtained by using low-cost imaging sensors.

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