scholarly journals GPS-based real-time location-aware system in outdoor environment

2007 ◽  
Vol 16 (4) ◽  
pp. 270-276
Author(s):  
Erin-Ee-Lin Lau ◽  
Kwang-Sig Shin ◽  
Wan-Young Chung
Keyword(s):  
Real Time ◽  
Outdoor Environment ◽  
Location Aware

scholarly journals RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3955
Author(s):  
Jung-Cheng Yang ◽  
Chun-Jung Lin ◽  
Bing-Yuan You ◽  
Yin-Long Yan ◽  
Teng-Hu Cheng
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
High Frequency ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Sampling Rate ◽  
Outdoor Environment ◽  
Position Accuracy ◽  
Graph Optimization ◽  
Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

scholarly journals Wall-E Surveyor Robot using Wireless Networks

2015 ◽  
Vol 4 (2) ◽  
pp. 143
Author(s):  
Aatish Chandak ◽  
Arjun Aravind ◽  
Nithin Kamath
Keyword(s):  
Wireless Networks ◽  
Real Time ◽  
Real World ◽  
Autonomous Navigation ◽  
Remote Access ◽  
Outdoor Environment ◽  
Ultrasonic Sensors ◽  
Moving Obstacles ◽  
Area Of Interest ◽  
World Environment

The methods for autonomous navigation of a robot in a real world environment is an area of interest for current researchers. Although there have been a variety of models developed, there are problems with regards to the integration of sensors for navigation in an outdoor environment like moving obstacles, sensor and component accuracy. This paper details an attempt to develop an autonomous robot prototype using only ultrasonic sensors for sensing the environment and GPS/ GSM and a digital compass for position and localization. An algorithm for the navigation based on reactive behaviour is presented. Once the robot has navigated to its final location based on remote access by the owner, it surveys the geographical region and uploads the real time images to the owner using an API that is developed for the Raspberry PI’s kernel.

scholarly journals Application of Auxiliary Classifier Wasserstein Generative Adversarial Networks in Wireless Signal Classification of Illegal Unmanned Aerial Vehicles

2018 ◽  
Vol 8 (12) ◽  
pp. 2664 ◽  
Author(s):  
Caidan Zhao ◽  
Caiyun Chen ◽  
Zeping He ◽  
Zhiqiang Wu
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Classification System ◽  
Outdoor Environment ◽  
Classification Model ◽  
Signal Classification ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Aerial Vehicles ◽  
Discriminative Model

Recently, many studies have reported on image synthesis based on Generative Adversarial Networks (GAN). However, the use of GAN does not provide much attention on the signal classification problem. In the context of using wireless signals to classify illegal Unmanned Aerial Vehicles (UAVs), this paper explores the feasibility of using GAN to improve the training datasets and obtain a better classification model, thereby improving the accuracy of classification. First, we use the generative model of GAN to generate a large datasets, which does not need manual annotation. At the same time, the discriminative model of GAN is improved to classify the types of signals based on the loss function of the discriminative model. Finally, this model can be used to the outdoor environment and obtain a real-time illegal UAVs signal classification system. Our experiments confirmed that the improvements on the Auxiliary Classifier Generative Adversarial Networks (AC-GANs) by limited datasets achieve excellent results. The recognition rate can reach more than 95% in the indoor environment, and this method is also applicable in the outdoor environment. Moreover, based on the theory of Wasserstein GANs (WGAN) and AC-GANs, a more robust Auxiliary Classifier Wasserstein GANs (AC-WGANs) model is obtained, which is suitable for multi-class UAVs. Through the combination of AC-WGANs and Universal Software Radio Peripheral (USRP) B210 software defined radio (SDR) platform, a real-time UAVs signal classification system is also implemented.

scholarly journals Real-Time 3D UAV Pose Estimation by Visualization

Proceedings ◽  
2020 ◽  
Vol 39 (1) ◽  
pp. 18
Author(s):  
Nenchoo ◽  
Tantrairatn
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Indoor Environment ◽  
Target Object ◽  
Outdoor Environment ◽  
Visual Object ◽  
Depth Camera ◽  
Positioning System ◽  
Stereo Camera ◽  
Local Positioning System

This paper presents an estimation of 3D UAV position in real-time condition by using Intel RealSense Depth camera D435i with visual object detection technique as a local positioning system for indoor environment. Nowadays, global positioning system or GPS is able to specify UAV position for outdoor environment. However, for indoor environment GPS hasn’t a capability to determine UAV position. Therefore, Depth stereo camera D435i is proposed to observe on ground to specify UAV position for indoor environment instead of GPS. Using deep learning for object detection to identify target object with depth camera to specifies 2D position of target object. In addition, depth position is estimated by stereo camera and target size. For experiment, Parrot Bebop2 as a target object is detected by using YOLOv3 as a real-time object detection system. However, trained Fully Convolutional Neural Networks (FCNNs) model is considerably significant for object detection, thus the model has been trained for bebop2 only. To conclude, this proposed system is able to specifies 3D position of bebop2 for indoor environment. For future work, this research will be developed and apply for visualized navigation control of drone swarm.

Depth-Based Real-Time Gait Recognition

2020 ◽  
Vol 29 (16) ◽  
pp. 2050266
Author(s):  
Adnan Ramakić ◽  
Diego Sušanj ◽  
Kristijan Lenac ◽  
Zlatko Bundalo
Keyword(s):  
Real Time ◽  
Gait Recognition ◽  
Video Stream ◽  
Outdoor Environment ◽  
Microsoft Kinect ◽  
Recognition Method ◽  
Real Time Processing ◽  
Depth Data ◽  
Video Images ◽  
Subject Identification

Each person describes unique patterns during gait cycles and this information can be extracted from live video stream and used for subject identification. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. In this paper, a method to enhance the appearance-based gait recognition method by also integrating features extracted from depth data is proposed. Two approaches are proposed that integrate simple depth features in a way suitable for real-time processing. Unlike previously presented works which usually use a short range sensors like Microsoft Kinect, here, a long-range stereo camera in outdoor environment is used. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing popular gait recognition methods.

A location-aware framework for intelligent real-time mobile applications

2011 ◽  
Vol 10 (3) ◽  
pp. 58-67 ◽  
Author(s):  
Sean J. Barbeau ◽  
Rafael A. Perez ◽  
Miguel A. Labrador ◽  
Alfredo J. Perez ◽  
Philip L. Winters ◽  
...  
Keyword(s):  
Real Time ◽  
Mobile Applications ◽  
Location Aware

Location-aware real time positioning with JinifMap

2010 ◽  
Author(s):  
Ilyoung Hong ◽  
Sunghak Song ◽  
Ju-hyun Park ◽  
Kyojun Koo
Keyword(s):  
Real Time ◽  
Location Aware
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