Integrated calibration between digital camera and laser scanner from mobile mapping system for land vehicles

2013 ◽  
Author(s):  
Guihua Zhao ◽  
Hong Chen ◽  
Xingquan Li ◽  
Xiaoliang Zou
Keyword(s):  
Laser Scanner ◽  
Digital Camera ◽  
Mobile Mapping ◽  
Land Vehicles ◽  
Mapping System ◽  
Mobile Mapping System

scholarly journals Validation of Portable Mobile Mapping System for Inspection Tasks in Thermal and Fluid–Mechanical Facilities

2019 ◽  
Vol 11 (19) ◽  
pp. 2205 ◽  
Author(s):  
Rodríguez-Martín ◽  
Rodríguez-Gonzálvez ◽  
Ruiz de Oña Crespo ◽  
González-Aguilera
Keyword(s):  
Three Dimensional ◽  
Laser Scanner ◽  
Ground Truth ◽  
Mobile Mapping ◽  
Industrial Facilities ◽  
Starting Point ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Machine Room ◽  
Inspection Tasks

The three-dimensional registration of industrial facilities has a great importance for maintenance, inspection, and safety tasks and it is a starting point for new improvements and expansions in the industrial facilities context. In this paper, a comparison between the results obtained using a novel portable mobile mapping system (PMMS) and a static terrestrial laser scanner (TLS), widely used for 3D reconstruction in civil and industrial scenarios, is carried out. This comparison is performed in the context of industrial inspection tasks, specifically in the thermal and fluid-mechanics facilities in a hospital. The comparison addresses the general reconstruction of a machine room, focusing on the quantitative and qualitative analysis of different elements (e.g., valves, regulation systems, burner systems and tanks, etc.). The validation of the PMMS is provided considering the TLS as ground truth and applying a robust statistical analysis. Results come to confirm the suitability of the PMMS to perform inspection tasks in industrial facilities.

1A2-Q15 Extrinsic Camera Parameter Estimation using Laser Scanner for a Mobile Mapping System(ITS and Robot Technology)

2011 ◽  
Vol 2011 (0) ◽  
pp. _1A2-Q15_1-_1A2-Q15_2
Author(s):  
Taro SUZUKI ◽  
Kiichiro ISHIKAWA ◽  
Yoshiharu AMANO ◽  
Takumi HASHIZUME ◽  
Yoshihiro SHIMA ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Laser Scanner ◽  
Mobile Mapping ◽  
Camera Parameter ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Robot Technology

scholarly journals Brief communication "Application of mobile laser scanning in snow cover profiling"

2011 ◽  
Vol 5 (1) ◽  
pp. 135-138 ◽  
Author(s):  
S. Kaasalainen ◽  
H. Kaartinen ◽  
A. Kukko ◽  
K. Anttila ◽  
A. Krooks
Keyword(s):  
Change Detection ◽  
Snow Cover ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
Mobile Mapping ◽  
Preliminary Results ◽  
Geodetic Institute ◽  
Mapping System ◽  
Mobile Mapping System

Abstract. We present a snowmobile-based mobile mapping system and its first application to snow cover roughness and change detection measurement. The ROAMER mobile mapping system, constructed at the Finnish Geodetic Institute, consists of the positioning and navigating systems, a terrestrial laser scanner, and the carrying platform (a snowmobile sledge in this application). We demonstrate the applicability of the instrument to snow cover roughness profiling and change detection by presenting preliminary results from a mobile laser scanning (MLS) campaign. The results show the potential of MLS for fast and efficient snow profiling from large areas in a millimetre scale.

scholarly journals MOBILE MAPPING OF THE LA CORONA LAVATUBE ON LANZAROTE

2019 ◽  
Vol IV-2/W5 ◽  
pp. 381-387 ◽  
Author(s):  
H. A. Lauterbach ◽  
D. Borrmann ◽  
A. Nüchter ◽  
A. P. Rossi ◽  
V. Unnithan ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Optimization Approach ◽  
Indoor Environments ◽  
Mobile Mapping ◽  
Trajectory Estimation ◽  
Mapping System ◽  
Laser Scanners ◽  
Mobile Mapping System ◽  
Mobile Mapping Systems ◽  
Mapping Systems

<p><strong>Abstract.</strong> Planetary surfaces consist of rough terrain and cave-like environments. Future planetary exploration demands for accurate mapping. However, recent backpack mobile mapping systems are mostly tested in structured, indoor environments. This paper evaluates the use of a backpack mobile mapping system in a cave-like environment. The experiments demonstrate the abilities of an continuous-time optimization approach by mapping part of a lavatube of the La Corona volcano system on Lanzarote. We compare two strategies for trajectory estimation relying either on 2D or 3D laser scanners and show that a 3D laser scanner substantially improved the final results.</p>

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.

scholarly journals Brief communication "Application of mobile laser scanning in snow cover profiling"

2010 ◽  
Vol 4 (4) ◽  
pp. 2513-2522 ◽  
Author(s):  
S. Kaasalainen ◽  
H. Kaartinen ◽  
A. Kukko ◽  
K. Anttila ◽  
A. Krooks
Keyword(s):  
Change Detection ◽  
Snow Cover ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
Mobile Mapping ◽  
Preliminary Results ◽  
Geodetic Institute ◽  
Mapping System ◽  
Mobile Mapping System

Abstract. We present a snowmobile based mobile mapping system and its first application on snow cover roughness and change detection measurement. The ROAMER mobile mapping system, constructed at the Finnish Geodetic Institute, consists of the positioning and navigating systems, a terrestrial laser scanner, and the carrying platform (a snowmobile sledge in this application). We demonstrate the applicability of the instrument in snow cover roughness profiling and change detection by presenting preliminary results from a mobile laser scanning (MLS) campaign. The results show the potential of MLS for fast and efficient snow profiling from large areas in a millimetre scale.

scholarly journals EVALUATION AND CALIBRATION OF FIXED-WING UAV MOBILE MAPPING SYSTEM EQUIPPED WITH LIDAR AND OPTICAL SENSORS

2018 ◽  
Vol XLII-1 ◽  
pp. 25-32 ◽  
Author(s):  
K. Bakuła ◽  
W. Ostrowski ◽  
M. Pilarska ◽  
M. Szender ◽  
Z. Kurczyński
Keyword(s):  
Optical Sensors ◽  
Near Infrared ◽  
Laser Scanner ◽  
Remote Sensing Data ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Mapping System ◽  
Vertical Error ◽  
Mobile Mapping System ◽  
Gnss Measurements

<p><strong>Abstract.</strong> In this paper, a mobile mapping system mounted on the UAV is presented and evaluated. The NEO3 UAV platform is an 11<span class="thinspace"></span>kg fixed-wing designed by the MSP company. The UAV is equipped with a Riegl miniVUX-1UAV laser scanner, which is integrated with the GNSS/INS system of Applanix APX-15 UAV and two Sony Alfa 6000 cameras collecting images in the following spectrum: visible for the first camera and near-infrared for the second camera. The UAV mobile system presented is dedicated to the acquisition of multisource data for levee monitoring using active and passive remote sensing data. In this paper, the effectiveness of the ultralight laser scanner, which has not been mounted on the fixed-wing platforms so far, was verified in the experiment with respect to data density and accuracy. The example analyses were conducted using ground control points and surfaces measured with a terrestrial laser scanner and visible in point clouds obtained with a dense image matching algorithm. Analyses showed that the achieved accuracy is much related to trajectory accuracy. The final DTM created from the data collected during the float status of the GNSS measurements of the trajectory provided twice less accurate data than during fixed status (vertical error approximately 20<span class="thinspace"></span>cm and 10<span class="thinspace"></span>cm respectively).</p>

scholarly journals CLASSIFICATION OF POLE-LIKE OBJECTS USING POINT CLOUDS AND IMAGES CAPTURED BY MOBILE MAPPING SYSTEMS

2018 ◽  
Vol XLII-2 ◽  
pp. 731-738 ◽  
Author(s):  
Y. Mori ◽  
K. Kohira ◽  
H. Masuda
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Target Object ◽  
Mobile Mapping ◽  
The Third ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Feature Values ◽  
3D Shapes

The vehicle-based mobile mapping system (MMS) is effective for capturing 3D shapes and images of roadside objects. The laser scanner and cameras on the MMS capture point-clouds and sequential digital images synchronously during driving. In this paper, we propose a method for detecting and classifying pole-like objects using both point-clouds and images captured using the MMS. In our method, pole-like objects are detected from point-clouds, and then target objects, which are objects attached to poles, are extracted for identifying the types of pole-like objects. For associating each target object with images, the points of the target object are projected onto images, and the image of the target object is cropped. Each pole-like object is represented as a feature vector, which are calculated from point-clouds and images. The feature values of a point-cloud are calculated by point processing, and the ones of the cropped image are calculated using a convolutional neural network. The feature values of point-clouds and images are unified, and they are used as the input to machine learning. In experiments, we classified pole-like objects using three methods. The first method used only point-clouds, the second used only images, and the third used both point-clouds and images. The experimental results showed that the third method could most accurately classify pole-like objects.

scholarly journals Generation of effective orthophotos for road surfaces using MMS

2014 ◽  
Vol XL-5 ◽  
pp. 621-628
Author(s):  
D. Yagishita ◽  
H. Chikatsu
Keyword(s):  
Laser Scanner ◽  
Road Surface ◽  
Mobile Mapping ◽  
Video Cameras ◽  
The Road ◽  
Lack Of Information ◽  
Mapping System ◽  
Road Surfaces ◽  
Mobile Mapping System ◽  
On The Road

In recent years, high precision and high resolution road surface orthophotos have been generated using video cameras mounted on surveying vehicles. However, there is a serious issue in generating an orthophoto from this image. The shadows of the surrounding structures and vehicles on the road surface cause a lack of information and decrease in visibility. Therefore, the shadows should be removed from the images for exact road management. On the other hand, the Mobile Mapping System with a laser scanner mounted on vehicles has been receiving more attention because the laser scanner intensity is almost unaffected by shadows. This paper presents shadow extraction and shadow correction for generating road surface orthophotos using the laser scanner intensity.

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