scholarly journals ROBUST RESECTION MODEL FOR ALIGNING THE MOBILE MAPPING SYSTEMS TRAJECTORIES AT DEGRADED AND DENIED URBAN ENVIRONMENTS

2020 ◽  
Vol V-1-2020 ◽  
pp. 215-222
Author(s):  
B. Alsadik
Keyword(s):  
Real Data ◽  
Experimental Tests ◽  
Urban Environments ◽  
Mobile Mapping ◽  
Positioning Accuracy ◽  
Pose Determination ◽  
Panoramic Images ◽  
The Absolute ◽  
Mobile Mapping Systems ◽  
Mapping Systems

Abstract. Mobile mapping systems MMS equipped with cameras and laser scanners are widely used nowadays for different geospatial applications with centimetric accuracy either in project wise or national wise scales. The achieved positioning accuracy is very much related to the navigation unit, namely the GNSS and IMU onboard. Accordingly, in GNSS denied and degraded environments, the absolute positioning accuracy is worsened to few meters in some cases. Frequently, ground control points GCPs of a high positioning accuracy are used to align the MMS trajectories and to improve the accuracy when needed.The best way to integrate the MMS trajectories to the GCPs is by measuring them on the MMS images where the positioning accuracy is dropped. MMS images are mostly spherical panoramic (equirectangular) images and sometimes perspective and, in both types, it is required to precisely determine the images orientation in what is called as space resection or camera pose determination. For perspective images, the pose is conventionally determined by collinearity equations or by using projection and fundamental matrices. Whereas for equirectangular panoramic images it is based on resecting vertical and horizontal angles. However, there is still a challenge in the state–of–the–art of image pose determination because of the model nonlinearity and the sensitivity to proper initialization and spatial distribution of the points.In this research, a generic method is presented to solve the pose resection problem for the perspective and equirectangular images using oblique angles. The oblique angles are derived from the measured image coordinates and based on spherical trigonometry rules and vector geometry. The developed algorithm has proven to be highly stable and steadily converge to the global minimum. This is related to the robust geometric constraint offered by the oblique angles that are enclosed between the object points and the camera. As a result, the MMS trajectories are realigned accurately to the GCPs and the absolute accuracy is highly refined. Four experimental tests are presented where the results show the efficiency of the proposed angular based model in different cases of simulated and real data with different image types.

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.

scholarly journals Examination of Indoor Mobile Mapping Systems in a Diversified Internal/External Test Field

2018 ◽  
Vol 8 (3) ◽  
pp. 401 ◽  
Author(s):  
Grazia Tucci ◽  
Domenico Visintini ◽  
Valentina Bonora ◽  
Erica Parisi
Keyword(s):  
Test Field ◽  
Mobile Mapping ◽  
External Test ◽  
Mobile Mapping Systems ◽  
Mapping Systems
Sign in / Sign up

Export Citation Format

Share Document