1P1-B09 A Study of Autonomous Mobile System in Outdoor Environment : Part 55 Development of Techniques for Road Surface Interpolation Using 3D Point Cloud and Sequential Images from Mobile Mapping System

2010 ◽  
Vol 2010 (0) ◽  
pp. _1P1-B09_1-_1P1-B09_2
Author(s):  
Tetsuya OGURA ◽  
Kiichiro ISHIKAWA ◽  
Jun-ichi TAKIGUCHI ◽  
Yoshiharu AMANO ◽  
Takumi HASHIZUME
Keyword(s):  
Point Cloud ◽  
Road Surface ◽  
Outdoor Environment ◽  
3D Point Cloud ◽  
Mobile System ◽  
Mobile Mapping ◽  
Surface Interpolation ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Sequential Images

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.

On the applicability of a scan-based mobile mapping system for monitoring the planarity and subsidence of road surfaces – Pilot study on the A44n motorway in Germany

2020 ◽  
Vol 14 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Erik Heinz ◽  
Christian Eling ◽  
Lasse Klingbeil ◽  
Heiner Kuhlmann
Keyword(s):  
Standard Deviation ◽  
Point Clouds ◽  
Road Surface ◽  
Control Points ◽  
Mobile Mapping ◽  
The Road ◽  
Mapping System ◽  
Road Surfaces ◽  
Mobile Mapping System ◽  
Physical Height

AbstractKinematic laser scanning is widely used for the fast and accurate acquisition of road corridors. In this context, road monitoring is a crucial application, since deficiencies of the road surface due to non-planarity and subsidence put traffic at risk. In recent years, a Mobile Mapping System (MMS) has been developed at the University of Bonn, consisting of a GNSS/IMU unit and a 2D laser scanner. The goal of this paper is to evaluate the accuracy and precision of this MMS, where the height component is of main interest. Following this, the applicability of the MMS for monitoring the planarity and subsidence of road surfaces is analyzed. The test area for this study is a 6 km long section of the A44n motorway in Germany. For the evaluation of the MMS, leveled control points along the motorway as well as point cloud comparisons of repeated passes were used. In order to transform the ellipsoidal heights of the MMS into the physical height system of the control points, undulations were utilized. In this respect, a local tilt correction for the geoid model was determined based on GNSS baselines and leveling, leading to a physical height accuracy of the MMS of < 10 mm (RMS). The related height precision has a standard deviation of about 5 mm. Hence, a potential subsidence of the road surface in the order of a few cm is detectable. In addition, the point clouds were used to analyze the planarity of the road surface. In the course of this, the cross fall of the road was estimated with a standard deviation of < 0.07 %. Yet, no deficiencies of the road surface in the form of significant rut depths or fictive water depths were detected, indicating the proper condition of the A44n motorway. According to our tests, the MMS is appropriate for road monitoring.

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.

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