System In Outdoor Environment Research Articles

A Lighting Consistency Technique for Outdoor Augmented Reality Systems Based on Multi-Source Geo-Information

Achieving seamless integration between virtual objects and real scenes has always been an important issue in augmented reality (AR) research. To achieve this, it is necessary to provide virtual objects with real-time and accurate lighting conditions from a real scene. Therefore, the purpose of this study is to realize lighting consistency rendering for real-time AR systems in outdoor environments, aiming to enhance the user’s sense of immersion. In this paper, we propose a lighting consistency technique for real-time AR systems in outdoor environments based on multi-source geographical information (MGI). Specifically, we introduce MGI into the study of lighting consistency and construct a comprehensive database to store and manage the acquired MGI data. Based on this, we proposed a sky radiance model driven using the MGI. Finally, we utilized the sky radiance model along with light sensor data to render the virtual objects in outdoor scenes. The experimental results show that the shadow angular error is reduced to 5.2°, and the system frame rate is increased to 94.26. This means that our method achieves a high level of realism in the fusion of virtual objects and real scenes while ensuring a high frame rate in the system. With this technology, users can conveniently and extensively realize the lighting consistency rendering of real-time AR systems in outdoor scenes using mobile devices.

Real-Time Parking Using AI & ML

Real time parking systems have a potential to reduce the congestion in crowded areas by providing real time indications of occupancy of parking spaces. To date, search systems are mostly implemented for indoor environments using costly sensor based techniques. Consequently, with the increasing demand for parking systems in outdoor environments, inexpensive image based detection methods have become a focus of research and development recently. Motivated by the remarkable performance of Convolutional Neural Networks (CNNs) in various image category recognition tasks, this study presents a robust parking occupancy detection framework by using a deep CNN and a binary Support Vector Machine (SVM) classifier to detect the occupancy of outdoor parking spaces images. The classifier was trained and tested why the features learned by the deep CNN (convolutional neural network) from public datasets (PKlot) having different illuminance and weather conditions. The indicates the great potential of this method to provide a low- cost and reliable solution to the parking systems in outdoor environments.

AWDMC-Net: Classification of Adversarial Weather Degraded Multiclass scenes using a Convolution Neural Network

Computer vision systems in outdoor environments are strongly affected by different atmospheric/weather conditions. Therefore, understanding the actual behavior of outdoor scenes is necessary for effective removal and improvement of the overall performance of computer vision systems. Although the classification of atmospheric/weather conditions has been well explored, reporting on the same in multiclass problem using Convolutional Neural Networks (CNNs) has received very little attention. In response to address this disparity, we propose a new CNN architecture named the “Adversarial Weather Degraded Multi-class scenes Classification Network (AWDMC-Net)” for outdoor scene classification degraded by different atmospheric/weather conditions. The proposed network is based on adopting different combinations of skip connections in building blocks of CNN there after adaptively pruning the least important convolutional kernels from the network. For effective pruning, we proposed a new pruning criterion named “Entropy Guided Mean-l1 Norm” that can adaptively evaluate the importance of convolutional kernels by considering the filters and their corresponding output feature maps. The prediction performance of our proposed model was evaluated on our newly designed E-TUVD (Extended Tripura University Video Dataset) and on publicly available benchmark datasets. Our newly created video dataset, E-TUVD, consists of 147 video clips (approximately 793800 frames) that represent six atmospheric/weather conditions, namely, fog, dust, rain, haze, poor illumination, and clear day conditions. Our proposed model achieves an accuracy of 93.85%, a specificity of 93.79%, and a sensitivity of 94.18% on our dataset, which outperforms the prevailing standard CNN models and recent state-of-the-art methods for atmospheric/weather classification tasks. Furthermore, our network also reduces the time consumption for atmospheric/weather classification tasks, and therefore mostly meets the requirements of practical applications in real-world scenarios.

Sensory Substitution for the Visually Impaired: A Study on the Usability of the Sound of Vision System in Outdoor Environments

For most visually impaired people, simple tasks such as understanding the environment or moving safely around it represent huge challenges. The Sound of Vision system was designed as a sensory substitution device, based on computer vision techniques, that encodes any environment in a naturalistic representation through audio and haptic feedback. The present paper presents a study on the usability of this system for visually impaired people in relevant environments. The aim of the study is to assess how well the system is able to help the perception and mobility of the visually impaired participants in real life environments and circumstances. The testing scenarios were devised to allow the assessment of the added value of the Sound of Vision system compared to traditional assistive instruments, such as the white cane. Various data were collected during the tests to allow for a better evaluation of the performance: system configuration, completion times, electro-dermal activity, video footage, user feedback. With minimal training, the system could be successfully used in outdoor environments to perform various perception and mobility tasks. The benefit of the Sound of Vision device compared to the white cane was confirmed by the participants and by the evaluation results to consist in: providing early feedback about static and dynamic objects, providing feedback about elevated objects, walls, negative obstacles (e.g., holes in the ground) and signs.

Method for Estimating Optimal Position of S-Band Relay Station through Path Loss Analysis in an Outdoor Environment

This article investigates a novel estimation method to optimize the position of a relay station of the S-band telemetry system in outdoor environments. To determine an optimal relay station position, complex terrain surface and buildings near the Naro Space Center are included in the EM (Electromagnetic) simulation. The optimal relay station position is then determined using the estimation method by comparing the path losses from the rocket to the main station and some candidate positions of the relay station. To better understand the path loss, the ray analysis is also conducted, and the result demonstrates that once the rocket altitude to be covered by the relay station is determined, it is possible to find the optimum position of the relay station.

Arduino-based low-cost electrical load tracking system with a long-range mesh network

A system that combines the advantage of the long-range (LoRa) communication method and the structural characteristics of a mesh network for an LoRa mesh network-based wireless electrical load tracking system is proposed. The system demonstrates considerable potential in reducing data loss due to environmental factors in far-field wireless energy monitoring. The proposed system can automatically control the function of each node by confirming the data source and eventually adjust the system structure according to real-time monitoring data without manual intervention. To further improve the sustainability of the system in outdoor environments, a standby equipment is designed to automatically ensure the normal operation of the system when the hardware of the base station fails. Our system is based on the Arduino board, which lowers the production cost and provides a simple manufacturing process. After conducting a long-term monitoring of a near-field smart manufacturing process in South Korea and the far-field energy consumption of rural households in Tanzania, we have proven that the system can be implemented in most regions, neither confined to a specific geographic location nor limited by the development of local infrastructure. This system comprises a smart framework that improves the quality of energy monitoring. Finally, the proposed big-data-technology-based power supply policy offers a new approach for prolonging the power supply time of off-grid power plants, thereby providing a guideline for more rural areas with limited power sources to utilize uninterrupted electricity.

Performance of Opportunistic Receiver Beam Selection in Multiaperture OWC Systems Over Foggy Channels

The atmospheric foggy condition is a major bottleneck for the deployment of optical wireless communication (OWC) systems in outdoor environments. Although the attenuation coefficient in the foggy channel follows the simple Gamma distribution, the distribution of its channel state is quite complicated similar to the well-studied Gamma–Gamma turbulence channel. Furthermore, existing literature suggested schemes that require feedback of the channel state information (CSI) to improve the outage performance of the OWC system under foggy conditions. In this article, we study the average signal-to-noise ratio (SNR), ergodic rate, and energy consumption performance of OWC systems under foggy conditions. First, we show that a single-aperture OWC system suffers significantly from the fog attenuation by deriving analytical expressions on the ergodic rate and energy consumption. Then, we consider a multiaperture OWC system equipped with an opportunistic receiver beam selection (RBS) scheme. The RBS scheme is based on the simple selection combining diversity algorithm, which does not require the knowledge of CSI. We analyze the proposed scheme by deriving computable bounds on the average SNR, ergodic rate, and energy consumption using system parameters and show the performance improvement of the RBS scheme comparing the single-aperture system. We demonstrate the performance of the considered scheme and validate derived expressions using computer simulations.

Adaptive preview control of single‐point path tracker for car‐like delivery service robot

The purpose of this research is to design a simple and intuitive path tracker to minimise the distance error of a car-like delivery service robot using only a position sensor, i.e. global positioning system in outdoor environments. The authors proposed not only a single-point preview path tracker (SPP), but also a curvature-based preview control using a quadratic curve fitting technique. This method can provide flexibility in preview distance control even at a constant speed, and reduces the burden of an actuator for maintenance. Furthermore, the performance of the proposed adaptive SPP is considerably improved compared to that when using a fixed preview distance.

Survey on Wireless Indoor Positioning Systems

Indoor positioning has finally testified a rise in interest, thanks to the big selection of services it is provided, and ubiquitous connectivity. There are currently many systems that can locate a person, be it wireless or by mobile phone and the most common systems in outdoor environments is the GPS, the most common in indoor environments is Wi-Fi positioning technique positioning. The improvement of positioning systems in indoor environments is desirable in many areas as it provides important facilities and services, such as airports, universities, factories, hospitals, and shopping malls. This paper provides an overview of the existing methods based on wireless indoor positioning technique. We focus in this survey on the strengths of these systems mentioned in the literature discordant with the present surveys; we also assess to additionally measure various systems from the scene of energy efficiency, price, and following accuracy instead of comparing the technologies, we also to additionally discuss residual challenges to correct indoor positioning.

Improved ITU‐R model for digital terrestrial television propagation path loss prediction

The use of a suitable propagation model for coverage prediction is key during the planning and dimensioning process of a digital terrestrial television (DTT) system. This study optimises one of the most detailed models for this purpose, i.e. Recommendation ITU-R.P.1812-4, with the aim of improving its accuracy in propagation path loss prediction for DTT systems in outdoor environments of Caracas city, Venezuela. For model adjustment, it is used the bioinspired technique particle swarm optimisation (PSO) and received signal level measurements performed in DTT systems in Caracas city under fixed reception conditions. The results, quantitatively analysed by RMS error and grey relation grade and mean absolute percentage error, shows a significant increase of the accuracy of optimised model in comparison with the original version and even with others propagation models.

Recognizing individuals in groups in outdoor environments combining stereo vision, RFID and BLE

Vision-based people localization systems in outdoor environments can be enhanced by means of radio frequency identification technologies. This combination has the potential to enable a wide range of new applications. When individuals wear a radio frequency tag, they may be both identified and localized. In this way, the technology may interact with individuals in a personalized way. In this paper, two radio frequency identification technologies, UHF Radio Frequency IDentification (RFID) and Bluetooth Low Energy (BLE), are combined with a stereo-based people detection system to recognize individuals in groups in complex outdoor scenarios in medium sized areas up to 20 m. The proposed approach is validated in crosswalks with pedestrians wearing portable RFID passive tags and active BLE beacons.

All-phase target reconstruction method for coherent field imaging

Received signal processing and target reconstruction technique plays a key role in coherent field imaging, and directly influences the quality of the reconstructed image of a target. Based on all-phase fast Fourier transformation (FFT) spectrum analysis theory, a new processing and reconstruction method is proposed. By directly extracting the phase and amplitude information from all-phase FFT spectrum of the return signal, the proposed all-phase target reconstruction method is capable of inhibiting the frequency error caused by many factors, thus reconstructing the target image more precisely. The validity of the proposed method is proved by a coherent field imaging system in outdoor environments, and it has a better reconstruction performance than a traditional method. The resolution of the reconstructed target is close to a theoretical diffraction limit.

Lessons learned: Evaluating visualizations for occluded objects in handheld augmented reality

Handheld devices like smartphones and tablets have emerged as one of the most promising platforms for Augmented Reality (AR). The increased usage of these portable handheld devices has enabled handheld AR applications to reach the end-users; hence, it is timely and important to seriously consider the user experience of such applications. AR visualizations for occluded objects enable an observer to look through objects. AR visualizations have been predominantly evaluated using Head-Worn Displays (HWDs), handheld devices have rarely been used. However, unless we gain a better understanding of the perceptual and cognitive effects of handheld AR systems, effective interfaces for handheld devices cannot be designed. Similarly, human perception of AR systems in outdoor environments, which provide a higher degree of variation than indoor environments, has only been insufficiently explored.In this paper, we present insights acquired from five experiments we performed using handheld devices in outdoor locations. We provide design recommendations for handheld AR systems equipped with visualizations for occluded objects. Our key conclusions are the following: (1) Use of visualizations for occluded objects improves the depth perception of occluded objects akin to non-occluded objects. (2) To support different scenarios, handheld AR systems should provide multiple visualizations for occluded objects to complement each other. (3) Visual clutter in AR visualizations reduces the visibility of occluded objects and deteriorates depth judgment; depth judgment can be improved by providing clear visibility of the occluded objects. (4) Similar to virtual reality interfaces, both egocentric and exocentric distances are underestimated in handheld AR. (5) Depth perception will improve if handheld AR systems can dynamically adapt their geometric field of view (GFOV) to match the display field of view (DFOV). (6) Large handheld displays are hard to carry and use; however, they enable users to better grasp the depth of multiple graphical objects that are presented simultaneously.

Design of Multi-Sensor-Based Open Architecture Integrated Navigation System for Localization of UGV

The UGV is one of the special field robot developed for mine detection, surveillance and transportation. To achieve successfully the missions of the UGV, the accurate and reliable navigation data should be provided. This paper presents design and implementation of multi-sensor-based open architecture integrated navigation for localization of UGV. The presented architecture hierarchically classifies the integrated system into four layers and data communications between layers are based on the distributed object oriented middleware. The navigation manager determines the navigation mode with the QoS information of each navigation sensor and the integrated filter performs the navigation mode-based data fusion in the filtering process. Also, all navigation variables including the filter parameters and QoS of navigation data can be modified in GUI and consequently, the user can operate the integrated navigation system more usefully. The conventional GPS/INS integrated system does not guarantee the long-term reliability of localization when GPS solution is not available by signal blockage and intentional jamming in outdoor environment. The presented integration algorithm, however, based on the adaptive federated filter structure with FDI algorithm can integrate effectively the output of multi-sensor such as 3D LADAR, vision, odometer, magnetic compass and zero velocity to enhance the accuracy of localization result in the case that GPS is unavailable. The field test was carried out with the UGV and the test results show that the presented integrated navigation system can provide more robust and accurate localization performance than the conventional GPS/INS integrated system in outdoor environments.

무인로봇 정밀위치추정을 위한 전술통신 및 영상 기반의 통합항법 성능 분석

This paper presents a navigation system based on the tactical communication and vision system in outdoor environments which is applied to unmanned robot for perimeter surveillance operations. GPS errors of robot are compensated by the reference station of C2(command and control) vehicle and WiBro(Wireless Broadband) is used for the communication between two systems. In the outdoor environments, GPS signals can be easily blocked due to trees and buildings. In this environments, however, vision system is very efficient because there are many features. With the feature MAP around the operation environments, the robot can estimate the position by the image matching and pose estimation. In the navigation system, thus, operation modes is switched by navigation manager according to some environment conditions. The experimental results show that the unmanned robot can estimate the position very accurately in outdoor environment.

1A1-D18 3次元LRFによる道路構造の理解に基づく屋外マッピングシステムの構築

This paper describes a mapping system in outdoor environments for a mobile robot. Our system can make a topological map based on understanding of road structures using a swinging LRF. The format Maps are graph structures whose nodes and arcs represent intersections and roads respectively. These maps can manage vast outdoor map with tiny computational resources. We implemented our system to a Personal Mobility Robot, and succeeded in making a outdoor map.

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

A location-aware system, which is capable to detect a user's whereabout in a region and provide real-time location information in outdoor environment is designed and fabricated, specifically using the GPS (global positioning system) as key technology in view of its stability and high precision in outdoor environment. Experiment is conducted to test this application in campus-based environment. Once GPS receiver detects specific location which is tagged with GPS coordinates, the system provides information relevant to that context and user can operate and react accordingly. Besides, this location-aware system is able to provide user with the route in order to get to a desired place by detecting the direction that the user is moving towards from his/her initial location. The mapping data is transferred via wireless LAN to PDA carried by the user as well as to the back-end system on real-time basis. The test result is analyzed and discussed to validate the effectiveness of using GPS in implementing this system and indicates future works that could be made to improve this prototype to be implemented in other applications, such as theme park, tourist attraction spot or recreational park.

1P2-N-083 屋外環境下で用いる自律移動システムに関する研究 : 第34報 三次元GISを用いたGPS衛星遮蔽を考慮した棄却処理によるマルチパス・回折波対策(3次元計測/センサフュージョン3,生活を支援するロボメカ技術のメガインテグレーション)

1P2-N-083 屋外環境下で用いる自律移動システムに関する研究 : 第34報 三次元GISを用いたGPS衛星遮蔽を考慮した棄却処理によるマルチパス・回折波対策(3次元計測/センサフュージョン3,生活を支援するロボメカ技術のメガインテグレーション)

A Study of Autonomous Mobile System in Outdoor Environment (Development of High-Resolution Positioning and Environmental Map Generating Systems)

This study aims to develop an autonomous mobile system in outdoor environment. The mechanism and algorithm for improving the robustness and flexibility of a mobile robot system is addressed. This paper describes a high-resolution-positioning system which has an automatic magnetic calibration system that corrects the local magnetic field error around a compass. This paper also describes a high-resolution environmental map generating system which has a polygon approximation function. It enables the system to renew the map periodically. The result shows that both systems have high accuracy and adaptability to the changeable environment.