scholarly journals Learning and SLAM Based Decision Support Platform for Sewer Inspection

2020 ◽  
Vol 12 (6) ◽  
pp. 968 ◽  
Author(s):  
Tzu-Yi Chuang ◽  
Cheng-Che Sung
Keyword(s):  
Low Cost ◽  
Autonomous Systems ◽  
Laser Scanner ◽  
Geometric Information ◽  
Depth Cameras ◽  
Safety Issues ◽  
Pipeline Inspection ◽  
Localization And Mapping ◽  
Additional Control ◽  
Sewer Inspection

Routine maintenance of drainage systems, including structure inspection and dredging, plays an essential role in disaster prevention and reduction. Autonomous systems have been explored to assist in pipeline inspection due to safety issues in unknown underground environments. Most of the existing systems merely rely on video records for visual examination since sensors such as a laser scanner or sonar are costly, and the data processing requires expertise. This study developed a compact platform for sewer inspection, which consisted of low-cost components such as infrared and depth cameras with a g-sensor. Except for visual inspection, the platform not only identifies internal faults and obstacles but also evaluates their geometric information, geo-locations, and the block ratio of a pipeline in an automated fashion. As the platform moving, the g-sensor reflects the pipeline flatness, while an integrated simultaneous localization and mapping (SLAM) strategy reconstructs the 3D map of the pipeline conditions simultaneously. In the light of the experimental results, the reconstructed moving trajectory achieved a relative accuracy of 0.016 m when no additional control points deployed along the inspecting path. The geometric information of observed defects accomplishes an accuracy of 0.9 cm in length and width estimation and an accuracy of 1.1% in block ratio evaluation, showing promising results for practical sewer inspection. Moreover, the labeled deficiencies directly increase the automation level of documenting irregularity and facilitate the understanding of pipeline conditions for management and maintenance.

scholarly journals Implementation of Slam, Navigation, Obstacle Avoidance, and Path Planning of a Robust Mobile Robot Using 2D Laser Scanner

2021 ◽  
Author(s):  
Omid Karimpour
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Low Cost ◽  
Laser Scanner ◽  
Memory Consumption ◽  
Localization And Mapping ◽  
Reliable Performance ◽  
Cost Efficient ◽  
Office Environments ◽  
Robotic Applications

Over the last decade, navigation and Simultaneous Localization and Mapping (SLAM) have become key players in developing robust mobile robots. Several SLAM approaches utilizing camera, laser scan, sonar and fusion of sensors were developed and improved by a number of researchers. In this thesis, comparisons of these methods were evaluated, especially those offering low cost benefits, and low computation and memory consumption. The aim of this thesis was to select the most reliable and cost-efficient approach for indoor autonomous robotic applications. Currently, there are numerous studies that have optimized these SLAM methods; however, they still suffer from various complications such as scale drifting and excessive computation. This study performed different experiments to observe these challenges in realworld environments. A modified Pioneer robot was used to implement the selected SLAM system and furthermore, perform obstacle avoidance and path planning in indoor office environments. The results and tests show the reliable performance of Gmapping after tuning its parameter and set right configurations.

scholarly journals Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields

2017 ◽  
Vol 36 (10) ◽  
pp. 1045-1052 ◽  
Author(s):  
Nived Chebrolu ◽  
Philipp Lottes ◽  
Alexander Schaefer ◽  
Wera Winterhalter ◽  
Wolfram Burgard ◽  
...  
Keyword(s):  
Sugar Beet ◽  
Large Scale ◽  
Autonomous Systems ◽  
Laser Scanner ◽  
Growth Stages ◽  
Agricultural Field ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Recorded Data ◽  
Agricultural Robot

There is an increasing interest in agricultural robotics and precision farming. In such domains, relevant datasets are often hard to obtain, as dedicated fields need to be maintained and the timing of the data collection is critical. In this paper, we present a large-scale agricultural robot dataset for plant classification as well as localization and mapping that covers the relevant growth stages of plants for robotic intervention and weed control. We used a readily available agricultural field robot to record the dataset on a sugar beet farm near Bonn in Germany over a period of three months in the spring of 2016. On average, we recorded data three times per week, starting at the emergence of the plants and stopping at the state when the field was no longer accessible to the machinery without damaging the crops. The robot carried a four-channel multi-spectral camera and an RGB-D sensor to capture detailed information about the plantation. Multiple lidar and global positioning system sensors as well as wheel encoders provided measurements relevant to localization, navigation, and mapping. All sensors had been calibrated before the data acquisition campaign. In addition to the data recorded by the robot, we provide lidar data of the field recorded using a terrestrial laser scanner. We believe this dataset will help researchers to develop autonomous systems operating in agricultural field environments. The dataset can be downloaded from http://www.ipb.uni-bonn.de/data/sugarbeets2016/ .

scholarly journals Implementation of Slam, Navigation, Obstacle Avoidance, and Path Planning of a Robust Mobile Robot Using 2D Laser Scanner

2021 ◽  
Author(s):  
Omid Karimpour
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Low Cost ◽  
Laser Scanner ◽  
Memory Consumption ◽  
Localization And Mapping ◽  
Reliable Performance ◽  
Cost Efficient ◽  
Office Environments ◽  
Robotic Applications

Over the last decade, navigation and Simultaneous Localization and Mapping (SLAM) have become key players in developing robust mobile robots. Several SLAM approaches utilizing camera, laser scan, sonar and fusion of sensors were developed and improved by a number of researchers. In this thesis, comparisons of these methods were evaluated, especially those offering low cost benefits, and low computation and memory consumption. The aim of this thesis was to select the most reliable and cost-efficient approach for indoor autonomous robotic applications. Currently, there are numerous studies that have optimized these SLAM methods; however, they still suffer from various complications such as scale drifting and excessive computation. This study performed different experiments to observe these challenges in realworld environments. A modified Pioneer robot was used to implement the selected SLAM system and furthermore, perform obstacle avoidance and path planning in indoor office environments. The results and tests show the reliable performance of Gmapping after tuning its parameter and set right configurations.

scholarly journals A V-SLAM Guided and Portable System for Photogrammetric Applications

2021 ◽  
Vol 13 (12) ◽  
pp. 2351
Author(s):  
Alessandro Torresani ◽  
Fabio Menna ◽  
Roberto Battisti ◽  
Fabio Remondino
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Motion Blur ◽  
Guidance System ◽  
Prototype System ◽  
Active Sensors ◽  
Localization And Mapping ◽  
Acquisition Speed ◽  
Mobile Mapping System ◽  
Slam Algorithm

Mobile and handheld mapping systems are becoming widely used nowadays as fast and cost-effective data acquisition systems for 3D reconstruction purposes. While most of the research and commercial systems are based on active sensors, solutions employing only cameras and photogrammetry are attracting more and more interest due to their significantly minor costs, size and power consumption. In this work we propose an ARM-based, low-cost and lightweight stereo vision mobile mapping system based on a Visual Simultaneous Localization And Mapping (V-SLAM) algorithm. The prototype system, named GuPho (Guided Photogrammetric System) also integrates an in-house guidance system which enables optimized image acquisitions, robust management of the cameras and feedback on positioning and acquisition speed. The presented results show the effectiveness of the developed prototype in mapping large scenarios, enabling motion blur prevention, robust camera exposure control and achieving accurate 3D results.

scholarly journals Molecularly Imprinted Polymer-Based Sensors for Priority Pollutants

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2406
Author(s):  
Mashaalah Zarejousheghani ◽  
Parvaneh Rahimi ◽  
Helko Borsdorf ◽  
Stefan Zimmermann ◽  
Yvonne Joseph
Keyword(s):  
Environmental Protection Agency ◽  
Selective Adsorption ◽  
Low Cost ◽  
Health And Safety ◽  
Priority Pollutants ◽  
Molecularly Imprinted ◽  
Innovative Strategies ◽  
Safety Issues ◽  
Long Term Stability ◽  
Wide Range

Globally, there is growing concern about the health risks of water and air pollution. The U.S. Environmental Protection Agency (EPA) has developed a list of priority pollutants containing 129 different chemical compounds. All of these chemicals are of significant interest due to their serious health and safety issues. Permanent exposure to some concentrations of these chemicals can cause severe and irrecoverable health effects, which can be easily prevented by their early identification. Molecularly imprinted polymers (MIPs) offer great potential for selective adsorption of chemicals from water and air samples. These selective artificial bio(mimetic) receptors are promising candidates for modification of sensors, especially disposable sensors, due to their low-cost, long-term stability, ease of engineering, simplicity of production and their applicability for a wide range of targets. Herein, innovative strategies used to develop MIP-based sensors for EPA priority pollutants will be reviewed.

scholarly journals Pipeline In-Line Inspection Method, Instrumentation and Data Management

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3862
Author(s):  
Qiuping Ma ◽  
Guiyun Tian ◽  
Yanli Zeng ◽  
Rui Li ◽  
Huadong Song ◽  
...  
Keyword(s):  
Data Management ◽  
Petroleum Products ◽  
Metal Loss ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Inspection Method ◽  
Safety Issues ◽  
Pipeline Inspection ◽  
Inspection Systems ◽  
Non Destructive

Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

scholarly journals Corrosion Study on Wellbore Materials for the CO2 Injection Process

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 115
Author(s):  
Le Quynh Hoa ◽  
Ralph Bäßler ◽  
Dirk Bettge ◽  
Enrico Buggisch ◽  
Bernadette Nicole Schiller ◽  
...  
Keyword(s):  
Cross Sections ◽  
Low Cost ◽  
Co2 Injection ◽  
Uniform Corrosion ◽  
Injection Wells ◽  
Safety Issues ◽  
Carbonation Process ◽  
Injection Process ◽  
Materials Used ◽  
Low Alloyed Steel

For reliability and safety issues of injection wells, corrosion resistance of materials used needs to be determined. Herein, representative low-cost materials, including carbon steel X70/1.8977 and low alloyed steel 1.7225, were embedded in mortar to mimic the realistic casing-mortar interface. Two types of cement were investigated: (1) Dyckerhoff Variodur commercial Portland cement, representing a highly acidic resistant cement and (2) Wollastonite, which can react with CO2 and become stable under a CO2 stream due to the carbonation process. Exposure tests were performed under 10 MPa and at 333 K in artificial aquifer fluid for up to 20 weeks, revealing crevice corrosion and uniform corrosion instead of expected pitting corrosion. To clarify the role of cement, simulated pore water was made by dispersing cement powder in aquifer fluid and used as a solution to expose steels. Surface analysis, accompanied by element mapping on exposed specimens and their cross-sections, was carried out to trace the chloride intrusion and corrosion process that followed.

scholarly journals Analysis of Uncertainty and Repeatability of a Low-Cost 3D Laser Scanner

Sensors ◽  
2012 ◽  
Vol 12 (7) ◽  
pp. 9046-9054 ◽  
Author(s):  
María-Eugenia Polo ◽  
Ángel M. Felicísimo
Keyword(s):  
Low Cost ◽  
Laser Scanner

scholarly journals Development and Performance Assessment of a Low-Cost UAV Laser Scanner System (LasUAV)

2018 ◽  
Vol 10 (7) ◽  
pp. 1094 ◽  
Author(s):  
Chiara Torresan ◽  
Andrea Berton ◽  
Federico Carotenuto ◽  
Ugo Chiavetta ◽  
Franco Miglietta ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Low Cost ◽  
Laser Scanner ◽  
And Performance

scholarly journals Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

2019 ◽  
Vol 93 (3) ◽  
pp. 411-429 ◽  
Author(s):  
Maria Immacolata Marzulli ◽  
Pasi Raumonen ◽  
Roberto Greco ◽  
Manuela Persia ◽  
Patrizia Tartarino
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Volume ◽  
Forest Plot ◽  
3D Point Clouds ◽  
Dense Point ◽  
Minimum Number ◽  
Scale Point

Abstract Methods for the three-dimensional (3D) reconstruction of forest trees have been suggested for data from active and passive sensors. Laser scanner technologies have become popular in the last few years, despite their high costs. Since the improvements in photogrammetric algorithms (e.g. structure from motion—SfM), photographs have become a new low-cost source of 3D point clouds. In this study, we use images captured by a smartphone camera to calculate dense point clouds of a forest plot using SfM. Eighteen point clouds were produced by changing the densification parameters (Image scale, Point density, Minimum number of matches) in order to investigate their influence on the quality of the point clouds produced. In order to estimate diameter at breast height (d.b.h.) and stem volumes, we developed an automatic method that extracts the stems from the point cloud and then models them with cylinders. The results show that Image scale is the most influential parameter in terms of identifying and extracting trees from the point clouds. The best performance with cylinder modelling from point clouds compared to field data had an RMSE of 1.9 cm and 0.094 m3, for d.b.h. and volume, respectively. Thus, for forest management and planning purposes, it is possible to use our photogrammetric and modelling methods to measure d.b.h., stem volume and possibly other forest inventory metrics, rapidly and without felling trees. The proposed methodology significantly reduces working time in the field, using ‘non-professional’ instruments and automating estimates of dendrometric parameters.

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