scholarly journals 3D Indoor Mapping System Using 2D LiDAR Sensor for Drones

2018 ◽  
Vol 7 (4.11) ◽  
pp. 179 ◽  
Author(s):  
M. R. Shahrin ◽  
F. H. Hashim ◽  
W. M.D.W. Zaki ◽  
A. Hussain ◽  
T. T. Raj
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Cost Effective ◽  
Sensor Data ◽  
3D Scanner ◽  
Mapping System ◽  
3D Scanners ◽  
Major Factors ◽  
3D Scans

Most 3D scanners are heavy, bulky and costly. These are the major factors that make them irrelevant to be attached to a drone for autonomous navigation. With modern technologies, it is possible to design a simple 3D scanner for autonomous navigation. The objective of this study is to design a cost effective 3D indoor mapping system using a 2D light detection and ranging (LiDAR) sensor for a drone. This simple 3D scanner is realised using a LiDAR sensor together with two servo motors to create the azimuth and elevation axes. An Arduino Uno is used as the interface between the scanner and computer for the real-time communication via serial port. In addition, an open source Point-Cloud Tool software is used to test and view the 3D scanner data. To study the accuracy and efficiency of the system, the LiDAR sensor data from the scanner is obtained in real-time in point-cloud form. The experimental results proved that the proposed system can perform the 2D and 3D scans with tolerable performance.  

Quantitative volumetrische Analyse der unteren Extremität: Validierung gegenüber etablierter Maßbandmessung und Wasserverdrängung

2018 ◽  
Vol 50 (06) ◽  
pp. 393-399 ◽  
Author(s):  
Konstantin Christoph Koban ◽  
Virginia Titze ◽  
Lucas Etzel ◽  
Konstantin Frank ◽  
Thilo Schenck ◽  
...  
Keyword(s):  
3D Scanner ◽  
3D Scanners ◽  
3D Scans

Zusammenfassung Hintergrund Zur Diagnostik und Therapieevaluation des Lip- und Lymphödems werden im klinischen Alltag weiterhin Maßbandmessungen eingesetzt. Hierbei werden ausgehend von standardisierten Umfangsmessungen im Bereich der betroffenen Extremitäten deren Volumina errechnet. Andere Verfahren wie Wasserverdrängung werden nicht regelhaft eingesetzt.Ziel dieser Studie war die Erprobung eines 3D Scanners als alternatives und reproduzierbares Tool zur objektiven Erfassung der Volumina der unteren Extremität. Patienten, Material und Methoden Wir führten an 20 Probanden 3D Scans der unteren Extremitäten mit einem handelsüblichen 3D Scanner, dem Artec Eva® durch, und errechneten das Volumen mit der dazugehörigen Software. Das Volumen der Extremitäten wurde zudem gemäß standardisierter Verfahren durch die Umfangsmethode (Konusmethode und Scheibenmethode) sowie per Wasserverdrängungstechnik bestimmt. Die Ergebnisse sowie Durchführungsdauer der drei Messmethoden wurde dokumentiert und statistisch ausgewertet. Ergebnisse Mittels 3D Volumetrie zeigten sich keine signifikanten Abweichungen zur Wasserverdrängung (p > 0,05). Die Konusmethode überschätzte signifikant das in der Wasserverdrängung und 3D Volumetrie gemessene Volumen deutlich. Die Scheibenmethode zeigte keine statistisch signifikanten Abweichungen, jedoch klinisch relevant hohe Abweichungen mit einer ausgeprägten Varianz im 95 % Konfidenzintervall. Alle Verfahren zeigten hohe positive Korrelationen zueinander. Die Wasserverdrängung zeigte sich mit dem größten zeitlichen Aufwand verbunden. Schlussfolgerung Unserer Ergebnisse nach Untersuchung von 40 unteren Extremitäten zeigen, dass durch 3D Scans und Software-basierte volumetrische Berechnung in kurzer Zeit objektive und reproduzierbare Ergebnisse erzielt werden können. Die Abweichung gegenüber dem Goldstandard

scholarly journals Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3096 ◽  
Author(s):  
Junfeng Xin ◽  
Shixin Li ◽  
Jinlu Sheng ◽  
Yongbo Zhang ◽  
Ying Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Sensor Fusion ◽  
Autonomous Navigation ◽  
Particle Swarm ◽  
Sensor Data ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Unmanned Surface Vehicles ◽  
Inertia Weight

Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.

scholarly journals A VERSATILE UAV NEAR REAL-TIME MAPPING SOLUTION FOR DISASTER RESPONSE – CONCEPT, IDEAS AND IMPLEMENTATION

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 429-435
Author(s):  
P. Fanta-Jende ◽  
D. Steininger ◽  
F. Bruckmüller ◽  
C. Sulzbachner
Keyword(s):  
Real Time ◽  
Disaster Response ◽  
Cost Effective ◽  
Semantic Segmentation ◽  
Sensor Technology ◽  
Mapping System ◽  
Stakeholder Perspective ◽  
Time Critical ◽  
Mapping Solution ◽  
Further Development

Abstract. In recent years, the proliferation and further development of unmanned aerial vehicles (UAVs) led to a great number of key technologies, advances and opportunities especially in the realm of time-critical applications. UAVs as a platform provide a unique combination of flexibility, affordability and sensor technology which enables the design of cost-effective and intriguing services particularly for disaster response. This contribution presents a concept for UAV-based near real-time mapping system for disaster relief to provide decision-making support for first responders particularly for possible disaster scenarios in Austria. We outline our system concept and its respective architecture, discuss requirements from a stakeholder perspective as well as legal regulations and initiatives at an EU level. In the methodology section of this paper, the preliminary data processing pipeline with respect to the near real-time orthomosaic generation and the semantic segmentation network are presented. Lastly, first experimental results of the pipeline are shown, and further advances are discussed.

scholarly journals AN INTEGRATED RAPID MAPPING SYSTEM FOR DISASTER MANAGEMENT

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 499-504 ◽  
Author(s):  
D. Hein ◽  
S. Bayer ◽  
R. Berger ◽  
T. Kraft ◽  
D. Lesmeister
Keyword(s):  
Real Time ◽  
Disaster Management ◽  
Large Scale ◽  
Software Tool ◽  
Aerial Images ◽  
Sensor Data ◽  
Camera System ◽  
Rapid Mapping ◽  
Mapping System ◽  
Effective Assessment

Natural disasters as well as major man made incidents are an increasingly serious threat for civil society. Effective, fast and coordinated disaster management crucially depends on the availability of a real-time situation picture of the affected area. However, in situ situation assessment from the ground is usually time-consuming and of limited effect, especially when dealing with large or inaccessible areas. A rapid mapping system based on aerial images can enable fast and effective assessment and analysis of medium to large scale disaster situations. This paper presents an integrated rapid mapping system that is particularly designed for real-time applications, where comparatively large areas have to be recorded in short time. The system includes a lightweight camera system suitable for UAV applications and a software tool for generating aerial maps from recorded sensor data within minutes after landing. The paper describes in particular which sensors are applied and how they are operated. Furthermore it outlines the procedure, how the aerial map is generated from image and additional gathered sensor data.

scholarly journals Fast 3D Semantic Mapping in Road Scenes

2019 ◽  
Vol 9 (4) ◽  
pp. 631 ◽  
Author(s):  
Xuanpeng Li ◽  
Dong Wang ◽  
Huanxuan Ao ◽  
Rachid Belaroussi ◽  
Dominique Gruyer
Keyword(s):  
3D Reconstruction ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Semantic Information ◽  
Computational Cost ◽  
Semantic Segmentation ◽  
Semantic Mapping ◽  
3D Point Cloud ◽  
Semantic Inference ◽  
Mapping System

Fast 3D reconstruction with semantic information in road scenes is of great requirements for autonomous navigation. It involves issues of geometry and appearance in the field of computer vision. In this work, we propose a fast 3D semantic mapping system based on the monocular vision by fusion of localization, mapping, and scene parsing. From visual sequences, it can estimate the camera pose, calculate the depth, predict the semantic segmentation, and finally realize the 3D semantic mapping. Our system consists of three modules: a parallel visual Simultaneous Localization And Mapping (SLAM) and semantic segmentation module, an incrementally semantic transfer from 2D image to 3D point cloud, and a global optimization based on Conditional Random Field (CRF). It is a heuristic approach that improves the accuracy of the 3D semantic labeling in light of the spatial consistency on each step of 3D reconstruction. In our framework, there is no need to make semantic inference on each frame of sequence, since the 3D point cloud data with semantic information is corresponding to sparse reference frames. It saves on the computational cost and allows our mapping system to perform online. We evaluate the system on road scenes, e.g., KITTI, and observe a significant speed-up in the inference stage by labeling on the 3D point cloud.

scholarly journals Static, Portable and Smart Detection of Water Quality in Panchayath Distribution Tank

2019 ◽  
Vol 8 (2S5) ◽  
pp. 103-108
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Cost Effective ◽  
Daily Basis ◽  
Quality Parameters ◽  
Sensor Data ◽  
Sensor Technology ◽  
Real Time System ◽  
Quality Of Water ◽  
Physical And Chemical

The significant crunch in the Current world is Water pollution. It has created an abundant influence on the Environment. With the intention of the non-toxic distribution of the water and its eminence should be monitored at real time. This paper suggested the smart detection with low cost real time system which is used to monitor the quality of water through IOT(internet of things). The system entail of different sensors which are used to measure the physical and chemical parameters of the water. The quality parameters are temperature, pH, turbidity, conductivity and Total dissolved solids of the water are measured. Commercially available products capable of monitoring such parameters are usually somewhat expensive and the data’s are collected by mobile van. Using Sensor technology provides a cost-effective and pre-eminent reliable as they can provide real time output. The measured values from the sensors can be observed by the core controller. The controller was programmed to monitor the distribution tank on a daily basis to hour basis monitoring. The TIVA C series is used as a core controller. The Controller is mounted on the side of the distribution tank. Finally, the sensor data from the controller is sent to Wi-Fi module through UART protocol. Wi-fi Module is connected to a public Wi-Fi system through which data is seen by the locals who are all connected to that Wi-Fi network.

scholarly journals Cost effective Parking System Using Computer Vision

2020 ◽  
pp. 241-246
Author(s):  
Kaushal Shah ◽  
Shivang Rajbhoi ◽  
Nikhil Prasad ◽  
Charmi Patel ◽  
Roushan Raj
Keyword(s):  
Real Time ◽  
Cost Effective ◽  
Video Stream ◽  
Sensor Data ◽  
Practical Implementation ◽  
Wireless System ◽  
Central System ◽  
Static Images ◽  
High Processing ◽  
Changing Light

This paper presents an approach for detecting real-time parking slots which includes vision-based techniques. Traditional sensor-based systems are not cost effective as 'n' number of sensors are required for 'n' parking slots. Transmitting sensor data to central system is done by hardwiring or installing dedicated wireless system which is again costly. Our technique will overcome this problem by using camera instead of number of sensors which is expensive. For detection we are using a Convolutional Neural Networks (CNN) classifier which is custom trained. It is more robust and effective in changing light conditions and weather. The following system do not require high processing as detections are done on static images not on video stream. We have also demonstrated real-time parking scenario by constructing a small prototype which shows practical implementation of our system.

scholarly journals A Real-Time 3D Perception and Reconstruction System Based on a 2D Laser Scanner

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Zheng Fang ◽  
Shibo Zhao ◽  
Shiguang Wen ◽  
Yu Zhang
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Low Cost ◽  
Laser Scanner ◽  
Continuous Mapping ◽  
Step Motor ◽  
3D Point Cloud ◽  
3D Perception ◽  
Mapping System ◽  
Local Point

This paper presents a real-time and low-cost 3D perception and reconstruction system which is suitable for autonomous navigation and large-scale environment reconstruction. The 3D mapping system is based on a rotating 2D planar laser scanner driven by a step motor, which is suitable for continuous mapping. However, for such a continuous mapping system, the challenge is that the range measurements are received at different times when the 3D LiDAR is moving, which will result in big distortion of the local 3D point cloud. As a result, the errors in motion estimation can cause misregistration of the resulting point cloud. In order to continuously estimate the trajectory of the sensor, we first extract feature points from the local point cloud and then estimate the transformation between current frame to local map to get the LiDAR odometry. After that, we use the estimated motion to remove the distortion of the local point cloud and then register the undistorted local point cloud to the global point cloud to get accurate global map. Finally, we propose a coarse-to-fine graph optimization method to minimize the global drift. The proposed 3D sensor system is advantageous due to its mechanical simplicity, mobility, low weight, low cost, and real-time estimation. To validate the performance of the proposed system, we carried out several experiments to verify its accuracy, robustness, and efficiency. The experimental results show that our system can accurately estimate the trajectory of the sensor and build a quality 3D point cloud map simultaneously.

scholarly journals UAV Obstacle Avoidance Algorithm to Navigate in Dynamic Building Environments

Drones ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 16
Author(s):  
Enrique Aldao ◽  
Luis M. González-deSantos ◽  
Humberto Michinel ◽  
Higinio González-Jorge
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Progress Monitoring ◽  
Flight Trajectory ◽  
Moving Obstacles ◽  
Current Implementation ◽  
Construction Progress Monitoring

In this work, a real-time collision avoidance algorithm was presented for autonomous navigation in the presence of fixed and moving obstacles in building environments. The current implementation is designed for autonomous navigation between waypoints of a predefined flight trajectory that would be performed by an UAV during tasks such as inspections or construction progress monitoring. It uses a simplified geometry generated from a point cloud of the scenario. In addition, it also employs information from 3D sensors to detect and position obstacles such as people or other UAVs, which are not registered in the original cloud. If an obstacle is detected, the algorithm estimates its motion and computes an evasion path considering the geometry of the environment. The method has been successfully tested in different scenarios, offering robust results in all avoidance maneuvers. Execution times were measured, demonstrating that the algorithm is computationally feasible to be implemented onboard an UAV.

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