scholarly journals Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS

2006 ◽  
Author(s):  
M. Agrawal ◽  
K. Konolige
Keyword(s):  
Real Time ◽  
Stereo Vision ◽  
Outdoor Environments

scholarly journals Stereo Vision Based Sensory Substitution for the Visually Impaired

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2771 ◽  
Author(s):  
Simona Caraiman ◽  
Otilia Zvoristeanu ◽  
Adrian Burlacu ◽  
Paul Herghelegiu
Keyword(s):  
Real Time ◽  
Stereo Vision ◽  
Visually Impaired ◽  
Large Scale ◽  
Sensory Substitution ◽  
Main Research ◽  
Visually Impaired People ◽  
Time Operation ◽  
Outdoor Environments ◽  
Real Time Operation

The development of computer vision based systems dedicated to help visually impaired people to perceive the environment, to orientate and navigate has been the main research subject of many works in the recent years. A significant ensemble of resources has been employed to support the development of sensory substitution devices (SSDs) and electronic travel aids for the rehabilitation of the visually impaired. The Sound of Vision (SoV) project used a comprehensive approach to develop such an SSD, tackling all the challenging aspects that so far restrained the large scale adoption of such systems by the intended audience: Wearability, real-time operation, pervasiveness, usability, cost. This article is set to present the artificial vision based component of the SoV SSD that performs the scene reconstruction and segmentation in outdoor environments. In contrast with the indoor use case, where the system acquires depth input from a structured light camera, in outdoors SoV relies on stereo vision to detect the elements of interest and provide an audio and/or haptic representation of the environment to the user. Our stereo-based method is designed to work with wearable acquisition devices and still provide a real-time, reliable description of the scene in the context of unreliable depth input from the stereo correspondence and of the complex 6 DOF motion of the head-worn camera. We quantitatively evaluate our approach on a custom benchmarking dataset acquired with SoV cameras and provide the highlights of the usability evaluation with visually impaired users.

scholarly journals Monitoring Real-time Personal Locomotion Behaviors over Smart Indoor-outdoor Environments via Body-Worn Sensors

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Munkhjargal Gochoo ◽  
Sheikh Badar Ud Din Tahir ◽  
Ahmad Jalal ◽  
Kibum Kim
Keyword(s):  
Real Time ◽  
Outdoor Environments

scholarly journals Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2534
Author(s):  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Autonomous Navigation ◽  
Control Technique ◽  
Local Motion ◽  
Aerial Robots ◽  
Novel Approach ◽  
Outdoor Environments ◽  
Aerial Vehicle ◽  
High Level

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

<i>Real-time Corn Grain Measurement Device Using Stereo Vision</i>

2019 ◽  
Author(s):  
Matthew Benjamin Rogers ◽  
Robert Clark Stevens
Keyword(s):  
Real Time ◽  
Stereo Vision ◽  
Measurement Device ◽  
Corn Grain

scholarly journals Learning-Type Anchors-Driven Pose Estimation for the Autolanding Fixed-Wing UAVs

2021 ◽  
Author(s):  
Dengqing Tang ◽  
Lincheng Shen ◽  
Xiaojiao Xiang ◽  
Han Zhou ◽  
Tianjiang Hu
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Stereo Vision ◽  
Vision System ◽  
Estimation Method ◽  
Satellite System ◽  
Training Dataset ◽  
Estimation Accuracy ◽  
The Impact ◽  
Outdoor Experiments

<p>We propose a learning-type anchors-driven real-time pose estimation method for the autolanding fixed-wing unmanned aerial vehicle (UAV). The proposed method enables online tracking of both position and attitude by the ground stereo vision system in the Global Navigation Satellite System denied environments. A pipeline of convolutional neural network (CNN)-based UAV anchors detection and anchors-driven UAV pose estimation are employed. To realize robust and accurate anchors detection, we design and implement a Block-CNN architecture to reduce the impact of the outliers. With the basis of the anchors, monocular and stereo vision-based filters are established to update the UAV position and attitude. To expand the training dataset without extra outdoor experiments, we develop a parallel system containing the outdoor and simulated systems with the same configuration. Simulated and outdoor experiments are performed to demonstrate the remarkable pose estimation accuracy improvement compared with the conventional Perspective-N-Points solution. In addition, the experiments also validate the feasibility of the proposed architecture and algorithm in terms of the accuracy and real-time capability requirements for fixed-wing autolanding UAVs.</p>

scholarly journals Formaldehyde and Glyoxal Measurement Deploying a Selected Ion Flow Tube Mass Spectrometer (SIFT-MS)

2021 ◽  
Author(s):  
Antonia Zogka ◽  
Manolis N. Romanias ◽  
Frederic Thevenet
Keyword(s):  
Proton Transfer ◽  
Real Time ◽  
Mass Spectrometer ◽  
Sharp Decrease ◽  
Ionization Mass ◽  
Flow Tube ◽  
Experimental Conditions ◽  
Ion Flow ◽  
Outdoor Environments ◽  
Selected Ion Flow Tube

Abstract. Formaldehyde (FM) and glyoxal (GL) are important atmospheric species of indoor and outdoor environments. They are either directly emitted in the atmosphere or they are formed through the oxidation of organic compounds by indoor and/or outdoor atmospheric oxidants. Despite their importance, the real-time monitoring of these compounds with soft ionization mass spectrometric techniques, e.g. proton transfer mass spectrometry (PTR-MS), remains problematic and is accompanied by low sensitivity. In this study, we evaluate the performance of a multi-ion selected ion flow tube mass spectrometer (SIFT-MS) to monitor in real-time atmospherically relevant concentrations of FM and GL under controlled experimental conditions. The SIFT-MS used is operated under standard conditions (SC), as proposed by the supplier, and customized conditions (CC), to achieve higher sensitivity. In the case of FM, SIFT-MS sensitivity is marginally impacted by RH, and the detection limits achieved are below 200 ppt. Contrariwise, in the case of GL, a sharp decrease of instrument sensitivity is observed with increasing RH when the H3O+ ion is used. Nevertheless, the detection of GL using NO+ precursor ion is moderately impacted by moisture with an actual positive sensitivity response. Therefore, we recommend the use of NO+ precursor for reliable detection and quantitation of GL. This work evidences that SIFT-MS can be considered as an efficient tool to monitor the concentration of FM and GL using SIFT-MS in laboratory experiments and potentially in indoor or outdoor environments. Furthermore, SIFT-MS technology still allows great possibilities for sensitivity improvement and high potential for monitoring low proton transfer affinity compounds.

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