scholarly journals Indoor Localization of Quadcopters in Industrial Environment

2020 ◽  
Author(s):  
Péter Troll ◽  
Károly Szipka ◽  
Andreas Archenti
Keyword(s):  
Computer Vision ◽  
Material Handling ◽  
Evaluation Method ◽  
Indoor Localization ◽  
Research Work ◽  
Fine Tuning ◽  
Measurement Unit ◽  
Positioning System ◽  
Estimation System ◽  
Slam Algorithm

The research work in this paper was carried out to reach advanced positioning capabilities of unmanned aerial vehicles (UAVs) for indoor applications. The paper includes the design of a quadcopter and the implementation of a control system with the capability to position the quadcopter indoor using onboard visual pose estimation system, without the help of GPS. The project also covered the design and implementation of quadcopter hardware and the control software. The developed hardware enables the quadcopter to raise at least 0.5kg additional payload. The system was developed on a Raspberry single-board computer in combination with a PixHawk flight controller. OpenCV library was used to implement the necessary computer vision. The Open-source software-based solution was developed in the Robotic Operating System (ROS) environment, which performs sensor reading and communication with the flight controller while recording data about its operation and transmits those to the user interface. For the vision-based position estimation, pre-positioned printed markers were used. The markers were generated by ArUco coding, which exactly defines the current position and orientation of the quadcopter, with the help of computer vision. The resulting data was processed in the ROS environment. LiDAR with Hector SLAM algorithm was used to map the objects around the quadcopter. The project also deals with the necessary camera calibration. The fusion of signals from the camera and from the IMU (Inertial Measurement Unit) was achieved by using Extended Kalman Filter (EKF). The evaluation of the completed positioning system was performed with an OptiTrack optical-based external multi-camera measurement system. The introduced evaluation method has enough precision to be used to investigate the enhancement of positioning performance of quadcopters, as well as fine-tuning the parameters of the used controller and filtering approach. The payload capacity allows autonomous material handling indoors. Based on the experiments, the system has an accurate positioning system to be suitable for industrial application.

scholarly journals Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4410 ◽  
Author(s):  
Faisal Jamil ◽  
Naeem Iqbal ◽  
Shabir Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Prediction Algorithm ◽  
Measurement Unit ◽  
Sensor Reading ◽  
Learning Module ◽  
Estimation System

Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.

scholarly journals A SINS/SAR/GPS Fusion Positioning System Based on Sensor Credibility Evaluations

2021 ◽  
Vol 13 (21) ◽  
pp. 4463
Author(s):  
Maoyou Liao ◽  
Jiacheng Liu ◽  
Ziyang Meng ◽  
Zheng You
Keyword(s):  
Standard Deviation ◽  
Evaluation Method ◽  
Measurement Unit ◽  
Positioning System ◽  
Sar Image ◽  
Chi Square ◽  
Positioning Systems ◽  
Variance Matrix ◽  
Actual Environment ◽  
The Difference

A reliable framework for SINS/SAR/GPS integrated positioning systems is proposed for the case that sensors are in critical environments. Credibility is used to describe the difference between the true error and the initial setting standard deviation. Credibility evaluation methods for inertial measurement unit (IMU), synthetic aperture radar (SAR), and global positioning system (GPS) are presented. In particular, IMU credibility is modeled by noises and constant drifts that are accumulated with time in a strapdown inertial navigation system (SINS). The quality of the SAR image decides the credibility of positioning based on SAR image matching. In addition, a cumulative residual chi-square test is used to evaluate GPS credibility. An extended Kalman filter based on a sensor credibility evaluation is introduced to integrate the measurements. The measurement of a sensor is either discarded when its credibility value is below a threshold or the variance matrix for the estimated state is otherwise adjusted. Simulations show that the final fusion positioning accuracy with credibility evaluation can be improved by 1–2 times compared to that without evaluation. In addition, the derived standard deviation correctly indicates the value of the position error with credibility evaluation. Moreover, the experiments on an unmanned ground vehicle partially verify the proposed evaluation method of GPS and the fusion framework in the actual environment.

scholarly journals Monocular indoor localization techniques for smartphones

2016 ◽  
Vol 8 (2) ◽  
pp. 186-215
Author(s):  
Gergely Hollósi ◽  
Csaba Lukovszki ◽  
István Moldován ◽  
Sándor Plósz ◽  
Frigyes Harasztos
Keyword(s):  
Computer Vision ◽  
Augmented Reality ◽  
Indoor Localization ◽  
State Of The Art ◽  
Research Work ◽  
Visual Localization ◽  
Promising Solution

Abstract In the last decade huge research work has been put to the indoor visual localization of personal smartphones. Considering the available sensor capabilities monocular odometry provides promising solution, even reecting requirements of augmented reality applications. This paper is aimed to give an overview of state-of-the-art results regarding monocular visual localization. For this purpose essential basics of computer vision are presented and the most promising solutions are reviewed.

Computer Vision and robotics in postal automation

1999 ◽  
Vol 18 (3-4) ◽  
pp. 265-273
Author(s):  
Giovanni B. Garibotto
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Material Handling ◽  
State Of The Art ◽  
Short Description ◽  
The Other ◽  
Functional Requirements ◽  
Postal Automation ◽  
And Robotics

The paper is intended to provide an overview of advanced robotic technologies within the context of Postal Automation services. The main functional requirements of the application are briefly referred, as well as the state of the art and new emerging solutions. Image Processing and Pattern Recognition have always played a fundamental role in Address Interpretation and Mail sorting and the new challenging objective is now off-line handwritten cursive recognition, in order to be able to handle all kind of addresses in a uniform way. On the other hand, advanced electromechanical and robotic solutions are extremely important to solve the problems of mail storage, transportation and distribution, as well as for material handling and logistics. Finally a short description of new services of Postal Automation is referred, by considering new emerging services of hybrid mail and paper to electronic conversion.

scholarly journals Measuring Gait Velocity and Stride Length with an Ultrawide Bandwidth Local Positioning System and an Inertial Measurement Unit

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2896
Author(s):  
Pratham Singh ◽  
Michael Esposito ◽  
Zach Barrons ◽  
Christian A. Clermont ◽  
John Wannop ◽  
...  
Keyword(s):  
Gold Standard ◽  
Gait Speed ◽  
Inertial Measurement Unit ◽  
Stride Length ◽  
Measurement Unit ◽  
Gait Velocity ◽  
Positioning System ◽  
Inertial Measurement ◽  
Measurement Systems ◽  
Local Positioning System

One possible modality to profile gait speed and stride length includes using wearable technologies. Wearable technology using global positioning system (GPS) receivers may not be a feasible means to measure gait speed. An alternative may include a local positioning system (LPS). Considering that LPS wearables are not good at determining gait events such as heel strikes, applying sensor fusion with an inertial measurement unit (IMU) may be beneficial. Speed and stride length determined from an ultrawide bandwidth LPS equipped with an IMU were compared to video motion capture (i.e., the “gold standard”) as the criterion standard. Ninety participants performed trials at three self-selected walk, run and sprint speeds. After processing location, speed and acceleration data from the measurement systems, speed between the last five meters and stride length in the last stride of the trial were analyzed. Small biases and strong positive intraclass correlations (0.9–1.0) between the LPS and “the gold standard” were found. The significance of the study is that the LPS can be a valid method to determine speed and stride length. Variability of speed and stride length can be reduced when exploring data processing methods that can better extract speed and stride length measurements.

Anti-Raider ATM System Using Mobilenetv2

2022 ◽  
Vol 9 (1) ◽  
pp. 0-0
Keyword(s):  
Computer Vision ◽  
Research Work ◽  
Computation Time ◽  
Vending Machines ◽  
Google Drive ◽  
Crime Counts

Cash vending machines are ubiquitous and although their technology vouches for its security, they are erratically stormed by the raiders. Albeit the escalating crime counts, the raiders are fleeing from the justice by virtue of evidence lacking. This research work proposes a computer vision based Anti-Raider ATM system. The proposed approach models the image, acquired from the CCTVs against the raider images based on the computer vision and deduces the fact from the MobileNetv2 architecture. Once the model identifies the raider, the image is uploaded to the Google Drive, which serves as evidence for the judicial department. The proposed research is modeled against several optimizers and the result concludes that, among them Adam optimizer has excelled in both computation time and accuracy.

scholarly journals A New Efficient Stiffness Evaluation Method to Improve Accuracy of Hexapods

2018 ◽  
Author(s):  
Vinayak J. Kalas ◽  
Alain Vissière ◽  
Thierry Roux ◽  
Olivier Company ◽  
Sébastien Krut ◽  
...  
Keyword(s):  
Evaluation Method ◽  
Positioning System ◽  
Positioning Error ◽  
Stiffness Parameter ◽  
Positioning Accuracy ◽  
Stiffness Evaluation ◽  
Improve Accuracy ◽  
Identification Technique ◽  
Six Degree Of Freedom ◽  
Structural Compliance

Structural compliance of hexapods limits their positioning accuracy. Taking a step towards solving this problem, this paper proposes a new efficient method to evaluate the stiffness of hexapods in order to predict and correct their positioning error due to compliance. The proposed method can be used to predict the six degree of freedom deflection of the platform under load. This method uses a simple lumped stiffness parameter model whose parameters can be estimated using the identification technique presented in this paper. An experimental study with micrometer level measurements performed on a hexapod based micro-positioning system is used to assess the efficiency of the presented method.

scholarly journals Aerial Imagery Paddy Seedlings Inspection Using Deep Learning

2022 ◽  
Vol 14 (2) ◽  
pp. 274
Author(s):  
Mohamed Marzhar Anuar ◽  
Alfian Abdul Halin ◽  
Thinagaran Perumal ◽  
Bahareh Kalantar
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Production Costs ◽  
Fine Tuning ◽  
Aerial Imagery ◽  
Planting Density ◽  
Deep Convolutional Neural Networks ◽  
Security Issues ◽  
Agriculture Industry ◽  
Paddy Cultivation

In recent years complex food security issues caused by climatic changes, limitations in human labour, and increasing production costs require a strategic approach in addressing problems. The emergence of artificial intelligence due to the capability of recent advances in computing architectures could become a new alternative to existing solutions. Deep learning algorithms in computer vision for image classification and object detection can facilitate the agriculture industry, especially in paddy cultivation, to alleviate human efforts in laborious, burdensome, and repetitive tasks. Optimal planting density is a crucial factor for paddy cultivation as it will influence the quality and quantity of production. There have been several studies involving planting density using computer vision and remote sensing approaches. While most of the studies have shown promising results, they have disadvantages and show room for improvement. One of the disadvantages is that the studies aim to detect and count all the paddy seedlings to determine planting density. The defective paddy seedlings’ locations are not pointed out to help farmers during the sowing process. In this work we aimed to explore several deep convolutional neural networks (DCNN) models to determine which one performs the best for defective paddy seedling detection using aerial imagery. Thus, we evaluated the accuracy, robustness, and inference latency of one- and two-stage pretrained object detectors combined with state-of-the-art feature extractors such as EfficientNet, ResNet50, and MobilenetV2 as a backbone. We also investigated the effect of transfer learning with fine-tuning on the performance of the aforementioned pretrained models. Experimental results showed that our proposed methods were capable of detecting the defective paddy rice seedlings with the highest precision and an F1-Score of 0.83 and 0.77, respectively, using a one-stage pretrained object detector called EfficientDet-D1 EficientNet.

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