scholarly journals A Simple and Efficient Registration of 3D Point Cloud and Image Data for Indoor Mobile Mapping System

2021 ◽  
Author(s):  
Hao Ma ◽  
keke Liu ◽  
Jingbin Liu ◽  
Hongyu Qiu ◽  
Dong Xu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
3D Point Cloud ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System

scholarly journals ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B1 ◽  
pp. 669-673
Author(s):  
Y. H. Li ◽  
T. Shinohara ◽  
T. Satoh ◽  
K. Tachibana
Keyword(s):  
Template Matching ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Mobile Mapping ◽  
Position Information ◽  
Road Signs ◽  
Road Sign ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Detection And Recognition

High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.

scholarly journals ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B1 ◽  
pp. 669-673 ◽  
Author(s):  
Y. H. Li ◽  
T. Shinohara ◽  
T. Satoh ◽  
K. Tachibana
Keyword(s):  
Template Matching ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Mobile Mapping ◽  
Position Information ◽  
Road Signs ◽  
Road Sign ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Detection And Recognition

High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.

scholarly journals AUTOMATIC DETECTION AND VECTORIZATION OF LINEAR AND POINT OBJECTS IN 3D POINT CLOUD AND PANORAMIC IMAGES FROM MOBILE MAPPING SYSTEM

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 305-312
Author(s):  
E. Barçon ◽  
A. Picard
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Automatic Detection ◽  
Lidar Data ◽  
3D Point Cloud ◽  
Mobile Mapping ◽  
Mapping System ◽  
Panoramic Images ◽  
Mobile Mapping System ◽  
Time Required

Abstract. Surveys of roadways with Mobile Laser Scanning (MLS) are nowadays the faster and more secured way to collect topographic data compared with conventional techniques. To deliver topographic plans, the voluminous data collected by the MLS device need to be processed. If the acquisition step is quite fast, the second part of interpretation and vectorization of the LiDAR data and the panoramic images is laborious and time consuming. This paper proposes two approaches that have been developed in order to reduce the time required to process roadway MLS data. The first one is about automatic detection of pole like objects, and the second one is about the detection of linear objects. The presented workflow try to automatically extract a 3D position for each object from MLS Data.

scholarly journals Creating an OpenDRIVE Model of the Campus of the Budapest University of Technology and Economics for Automotive Simulations

2020 ◽  
Author(s):  
Árpád Barsi ◽  
Vivien Potó ◽  
János Máté Lógó ◽  
Nikol Krausz
Keyword(s):  
Point Cloud ◽  
Virtual Model ◽  
Mobile Mapping ◽  
Environmental Models ◽  
University Of Technology ◽  
Mapping System ◽  
Road Geometry ◽  
Driving Scenario ◽  
Mobile Mapping System ◽  
New Sensors

The development of automotive technologies requires quite a significant amount of time and money. To accelerate this procedure, the technology of now is strongly based on computer simulations, where the whole vehicle or its parts can be analyzed in a virtual environment. The behavior of cars, especially equipped with new sensors or assistants, requires long testing, where the automotive simulators can play a cardinal role. The precise vehicular tests request accurate environmental models. These new kinds of models are still standardized; one of the pioneer de facto standards is OpenDRIVE. This standard was initially defined to be able to express all elements with all potential parameters required in high precision simulations. The actual research focused on creating a compliant virtual model based on mobile mapping measurements. A Leica Pegasus Two mobile mapping system was applied to capture field data about the selected pilot area, which is the campus of Budapest University of Technology and Economics (BME). The obtained Lidar point cloud was georeferenced; the merged point cloud is tailored to the driven trajectory, and then it has been evaluated manually. The acquired land use map is converted – similarly manually – into basic road geometry elements: straight lane and bended lane segments. These objects are finally compiled into an XML format, which is compliant with the OpenDRIVE standard. The achieved virtual model has been tested in Driving Scenario Designer of Mathworks Matlab; however, it is promptly ready for use in other widely applied automotive simulators.

scholarly journals TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA

2015 ◽  
Vol XL-4/W5 ◽  
pp. 13-18 ◽  
Author(s):  
M. Nakagawa ◽  
T. Yamamoto ◽  
S. Tanaka ◽  
M. Shiozaki ◽  
T. Ohhashi
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Indoor Environment ◽  
Lidar Data ◽  
Range Image ◽  
Mobile Mapping ◽  
Massive Point ◽  
Surface Estimation ◽  
Mapping System ◽  
Mobile Mapping System

We focus on a region-based point clustering to extract a polygon from a massive point cloud. In the region-based clustering, RANSAC is a suitable approach for estimating surfaces. However, local workspace selection is required to improve a performance in a surface estimation from a massive point cloud. Moreover, the conventional RANSAC is hard to determine whether a point lies inside or outside a surface. In this paper, we propose a method for panoramic rendering-based polygon extraction from indoor mobile LiDAR data. Our aim was to improve region-based point cloud clustering in modeling after point cloud registration. First, we propose a point cloud clustering methodology for polygon extraction on a panoramic range image generated with point-based rendering from a massive point cloud. Next, we describe an experiment that was conducted to verify our methodology with an indoor mobile mapping system in an indoor environment. This experiment was wall-surface extraction using a rendered point cloud from some viewpoints over a wide indoor area. Finally, we confirmed that our proposed methodology could achieve polygon extraction through point cloud clustering from a complex indoor environment.

scholarly journals Integration of a Wearable Mobile Mapping Solution and Advance Numerical Simulations for the Structural Analysis of Historical Constructions: A Case of Study in San Pedro Church (Palencia, Spain)

2021 ◽  
Vol 13 (7) ◽  
pp. 1252
Author(s):  
Luis Javier Sánchez-Aparicio ◽  
Rocío Mora ◽  
Borja Conde ◽  
Miguel Ángel Maté-González ◽  
María Sánchez-Aparicio ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Point Cloud ◽  
Mobile Mapping ◽  
Structural Problems ◽  
Mapping System ◽  
Cad Models ◽  
Mobile Mapping System ◽  
Simultaneous Location And Mapping ◽  
Time Required ◽  
Mapping Solution

This work aims at enhancing the current methodologies used for generating as-built CAD models suitable for advanced numerical simulations. To this end, this paper proposes the use of a wearable mobile mapping system that allows one to improve the digitalization stage in terms of flexibility and time required. The noise showed by the resulting point cloud, based on the simultaneous location and mapping (SLAM) solution, demands a post-processing stage that introduces the use of a parameter-free noise reduction filter. This filter improves the quality of the point cloud, allowing for the adjustment of surfaces by means of parametric and non-parametric shapes. These shapes are created by using reverse engineering procedures. The results showed during this investigation highlight a novel application of this sensor: the creation of as-built CAD models for advanced numerical simulations. The results of this investigation are complemented by a valuable contribution with respect to the use of an advanced restoration solution, by means of textile reinforced mortar. To this end, the CAD model is used as the geometrical base for several numerical simulations by means of the finite element method. All this procedure is applied in a construction with structural problems.

scholarly journals Automated Accuracy Assessment of a Mobile Mapping System with Lightweight Laser Scanning and MEMS Sensors

2021 ◽  
Vol 11 (3) ◽  
pp. 1007
Author(s):  
Kaleel Al-Durgham ◽  
Derek D. Lichti ◽  
Eunju Kwak ◽  
Ryan Dixon
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Laser Scanner ◽  
Test Facility ◽  
Point Cloud Data ◽  
Mobile Mapping ◽  
Cloud Data ◽  
Mapping System ◽  
Mobile Mapping System ◽  
The Mean

The accuracy assessment of mobile mapping system (MMS) outputs is usually reliant on manual labor to inspect the quality of a vast amount of collected geospatial data. This paper presents an automated framework for the accuracy assessment and quality inspection of point cloud data collected by MMSs operating with lightweight laser scanners and consumer-grade microelectromechanical systems (MEMS) sensors. A new, large-scale test facility has been established in a challenging navigation environment (downtown area) to support the analyses conducted in this research work. MMS point cloud data are divided into short time slices for comparison with the higher-accuracy, terrestrial laser scanner (TLS) point cloud of the test facility. MMS data quality is quantified by the results of registering the point cloud of each slice with the TLS datasets. Experiments on multiple land vehicle MMS point cloud datasets using a lightweight laser scanner and three different MEMS devices are presented to demonstrate the effectiveness of the proposed method. The mean accuracy of a consumer grade MEMS (<$100) was found to be 1.13 ± 0.47 m. The mean accuracy of two commercial MEMS (>$100) was in the range of 0.48 ± 0.23 m to 0.85 ± 0.52 m. The method presented here in can be straightforwardly implemented and adopted for the accuracy assessment of other MMSs types such as unmanned aerial vehicles (UAV)s.

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