2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)最新文献_第2页

JUPITER – ROS based Vehicle Platform for Autonomous Driving Research 基于JUPITER - ROS的自动驾驶研究车辆平台

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-11-14 DOI: 10.1109/ROSE56499.2022.9977434

Johann Haselberger, Marcel Pelzer, B. Schick, S. Müller

引用次数: 1

Data-Driven Hardware-in-the-Loop Plant Modeling for Self-Driving Vehicles 数据驱动的自动驾驶汽车硬件在环工厂建模

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-11-14 DOI: 10.1109/ROSE56499.2022.9977411

Hannah Grady, Nicholas Nauman, Md. Suruz Miah

{"title":"Data-Driven Hardware-in-the-Loop Plant Modeling for Self-Driving Vehicles","authors":"Hannah Grady, Nicholas Nauman, Md. Suruz Miah","doi":"10.1109/ROSE56499.2022.9977411","DOIUrl":"https://doi.org/10.1109/ROSE56499.2022.9977411","url":null,"abstract":"In this paper, we present data-driven hardware-in-the-loop (HIL) plant models of different subsystems of a self-driving vehicle. Despite numerous concerns, the automotive industry is still investing remarkable resources into the production of self-driving vehicles. Among the challenges in the development process of such vehicles are the validation and testing process of various subsystems. Here we provide data-driven models of different subsystems so that the automotive industry can validate and test autonomous vehicles without the need of a physical vehicle, which would reduce the considerable amount of cost to the automotive industry. The vehicle subsystems considered in this work include the steering, acceleration, brake, shift, speed, and speed control subsystems. Each of these subsystems is either a multi-input single output or single-input single output system. A Lexus RX450H self-driving vehicle is employed to collect raw data (inputs and outputs data for different subsystems) offline. We used the deep learning toolbox available in the commercial software package, MATLAB/SIMULINK, for modeling each of these systems. The contribution of this paper is twofold. First, collecting real time raw data from a physical Lexus RX450H vehicle and using it to develop machine learning models to represent the vehicle subsystems. Second, subsystem models created using machine learning tools for the Lexus vehicle are tested using Hardware-in-the-Loop. Therefore, the results of such modeling could be used for validation and testing without the need for a physical self-driving vehicle. The proposed modeling results could be useful for reducing the cost of the vehicle development process, since a physical vehicle is not required for validation and testing.","PeriodicalId":265529,"journal":{"name":"2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116451897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vision based sidewalk recognition for walking assistive robot in outdoor environments 基于视觉的户外行走辅助机器人人行道识别

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-11-14 DOI: 10.1109/ROSE56499.2022.9977414

Kodai Oyake, Goragod Pongthanisorn, Aya Shirai, S. Kaneko, G. Capi

引用次数: 0

Transformers for Imbalanced Baggage Threat Recognition 用于不平衡行李威胁识别的变压器

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-11-14 DOI: 10.1109/ROSE56499.2022.9977427

D. Velayudhan, Abdelfatah Hassan Ahmed, Taimur Hassan, Bennamoun, E. Damiani, N. Werghi

{"title":"Transformers for Imbalanced Baggage Threat Recognition","authors":"D. Velayudhan, Abdelfatah Hassan Ahmed, Taimur Hassan, Bennamoun, E. Damiani, N. Werghi","doi":"10.1109/ROSE56499.2022.9977427","DOIUrl":"https://doi.org/10.1109/ROSE56499.2022.9977427","url":null,"abstract":"Baggage screening for identifying concealed threat items has become inevitable for maintaining public security at high-risk locations, including airports and border checkpoints. However, manual screening needs both expertise and experience, in addition to being cumbersome and prone to errors, encouraging researchers to invest in developing autonomous baggage screening systems. However, these approaches based on CNNs prioritize localized interactions due to their solid inductive bias, restricting their ability to model object-level and image-wide context. Hence, in this paper, we explore Transformers for baggage threat recognition to exploit their ability to model global features to capture concealed threat items within cluttered and tightly packed baggage scans and thereby learn enhanced representations to identify the abnormal scans. Further, the property of visual transformers to prioritize shape over textural information render them a suitable candidate for threat recognition from baggage scans since they lack texture and have low contrast. We also explore the potential of visual transformers in heavily imbalanced settings. Further, we have also implemented a weakly supervised localization approach to identify the input regions contributing to the abnormality classification. The proposed approach surpasses the state-of-art methods achieving 0.979 on Compass-XP, and 0.873 on SIXray, in terms of F1 score.","PeriodicalId":265529,"journal":{"name":"2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132420153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Dilated Convolution and Residual Network based Convolutional Neural Network for Recognition of Disastrous Events 基于扩展卷积和残差网络的卷积神经网络识别灾难性事件

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-11-14 DOI: 10.1109/ROSE56499.2022.9977424

Dania Shafique, M. Akram, Taimur Hassan, Tahira Anwar, A. A. Salam

{"title":"Dilated Convolution and Residual Network based Convolutional Neural Network for Recognition of Disastrous Events","authors":"Dania Shafique, M. Akram, Taimur Hassan, Tahira Anwar, A. A. Salam","doi":"10.1109/ROSE56499.2022.9977424","DOIUrl":"https://doi.org/10.1109/ROSE56499.2022.9977424","url":null,"abstract":"Natural disasters include earthquakes, land sliding, floods, and typhoons causing great damage to manmade structures. To efficiently manage natural disasters, it is important to develop an automatic disaster recognition system based on deep learning algorithms such as Convolutional Neural Network. This research aims to introduce the application of Deep learning algorithms in the recognition of different disasters such as building collapse, and burning buildings caused by earthquakes and fire respectively. In this research, a novel approach using a single deep convolution neural network is implemented based on two main characteristics e.g. dilated convolution in which convolution is applied on an input image using defined gaps to capture more contextual information and fine details, and residual connection in which the input layer is not only connected to adjacent layer but maybe the summation of previous layers to reduce the problem of vanishing gradient. Dilated Residual Network is trained and tested on publicly available datasets of disasters that are NWPU-RESICS45, BOWFire, and Satellite image of Hurricane Damage, and Accident Image Analysis dataset that achieved testing accuracy of 92.06%, 76%, 98.15%, and 93.16% respectively. As there is a lack of a single disastrous event dataset so images of disasters were collected using different web scraping tools. These tools allow downloading images in bulk. After discarding irrelevant images disastrous event dataset consists of four classes having 10,000 images each. DRN was applied to the disastrous event dataset and the result showed model achieved 95.67% testing accuracy. The results proved that the proposed methodology is efficient enough and can be generalized for other disaster classification problems.","PeriodicalId":265529,"journal":{"name":"2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120979285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Catch-Me-If-You-Can Infrastructure-less UWB-based Leader-Follower System for Compact UAVs 紧凑型无人机的无基础设施超宽带领导-跟随系统

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-11-14 DOI: 10.1109/ROSE56499.2022.9977417

L. Santoro, M. Nardello, Marco Calliari, William Cechin Guarienti

引用次数: 3

Image Classification and Text Identification in Inspecting Military Aircrafts Logos: Application of Convolutional Neural Network 军机徽标检测中的图像分类与文本识别:卷积神经网络的应用

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-11-14 DOI: 10.1109/ROSE56499.2022.9977418

S. Edhah, Abeer Awadallah, Mayar Madboly, Hamdihun Dawed, N. Werghi

{"title":"Image Classification and Text Identification in Inspecting Military Aircrafts Logos: Application of Convolutional Neural Network","authors":"S. Edhah, Abeer Awadallah, Mayar Madboly, Hamdihun Dawed, N. Werghi","doi":"10.1109/ROSE56499.2022.9977418","DOIUrl":"https://doi.org/10.1109/ROSE56499.2022.9977418","url":null,"abstract":"Object detection and inspection using images or videos have been receiving increased attention in many applications such as traffic control, brand monitoring, trademark compliance, and product authentication. A particular application that is currently a topic of interest is aircraft logo detection, which aims at automating the visual inspection carried out manually by aircraft engineers. Aircraft logos should meet a large set of requirements that include geometric constraints on the logo elements and patterns, and constraints on the position and orientation with respect to specific references. This work considers the design of a high accuracy convolutional neural network to detect and classify aircraft logos as either adequate or inadequate based on specified criteria. The performance of the developed network is compared to a number of classical machine learning algorithms to demonstrate its effectiveness. Adequate logos are then processed further by extracting them from a frame using robust features extraction algorithm and determining their orientation angle with respect to the horizontal reference axis. Afterward, a text detection technique using a character region awareness for text detection algorithm implemented on a pre-trained network is carried out, along with optical character recognition tool to detect and extract the text from the logos for further processing in other applications. The developed network is tested on actual aircraft logos, captured from the field, where satisfactory results are obtained.","PeriodicalId":265529,"journal":{"name":"2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133995504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image-based Obstacle Avoidance using 3DConv Network for Rocky Environment 基于图像的岩石环境3DConv网络避障

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-11-14 DOI: 10.1109/ROSE56499.2022.9977423

Abderrahmene Boudiaf, A. Sumaiti, J. Dias

{"title":"Image-based Obstacle Avoidance using 3DConv Network for Rocky Environment","authors":"Abderrahmene Boudiaf, A. Sumaiti, J. Dias","doi":"10.1109/ROSE56499.2022.9977423","DOIUrl":"https://doi.org/10.1109/ROSE56499.2022.9977423","url":null,"abstract":"Autonomous navigation systems are an essential part of Unmanned Ground Vehicles (UGVs) since they allow for navigating without supervision in conditions where communication is not available or the existence of high delays which prevent direct communication. One fundamental part of autonomous navigation is obstacle avoidance. Typical approaches utilize some form of distance measuring-based sensors like LIDAR or SONAR. However, such devices have a relatively higher cost in comparison to conventional RGB cameras in addition to introducing complexity in data processing which results in an increase in computational cost and power consumption. In this work, we use sequential RGB data and a Conv3d-based network to create a real-time obstacle avoidance system with high accuracy, low latency, and low processing cost. For training, we used Unreal Engine 4 based simulator to collect a dataset to train the network. Testing the system in a simulated environment using the same simulator showed the ability of the network to avoid obstacles in a realistic environment where rocks of different sizes and shapes were used. Future work can include improving in terms of performance and processing time as well as implementing the network with a real word working prototype and comparing the simulated results with actual performance.","PeriodicalId":265529,"journal":{"name":"2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126919562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Learning-Based Approach for Bias Elimination in Low-Cost Gyroscopes 一种基于学习的低成本陀螺仪偏差消除方法

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-10-10 DOI: 10.1109/ROSE56499.2022.9977422

Daniel Engelsman, I. Klein

引用次数: 4

Towards Multidimensional Textural Perception and Classification Through Whisker 基于晶须的多维纹理感知与分类

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-09-01 DOI: 10.1109/ROSE56499.2022.9977409

P. K. Routray, A. Kanade, P. Pounds, Manivannan Muniyandi

{"title":"Towards Multidimensional Textural Perception and Classification Through Whisker","authors":"P. K. Routray, A. Kanade, P. Pounds, Manivannan Muniyandi","doi":"10.1109/ROSE56499.2022.9977409","DOIUrl":"https://doi.org/10.1109/ROSE56499.2022.9977409","url":null,"abstract":"Texture-based studies and designs have been in focus recently. Whisker-based multidimensional surface texture data is missing in the literature. This data is critical for robotics and machine perception algorithms in the classification and regression of textural surfaces. In this study, we present a novel sensor design to acquire multidimensional texture information. The surface texture's roughness and hardness were measured experimentally using sweeping and dabbing. Three machine learning models (SVM, RF, and MLP) showed excellent classification accuracy for the roughness and hardness of surface textures. We show that the combination of pressure and accelerometer data, collected from a standard machined specimen using the whisker sensor, improves classification accuracy. Further, we experimentally validate that the sensor can classify texture with roughness depths as low as $2.5mu m$ at an accuracy of 90% or more and segregate materials based on their roughness and hardness. We present a novel metric to consider while designing a whisker sensor to guarantee the quality of texture data acquisition beforehand. The machine learning model performance was validated against the data collected from the laser sensor from the same set of surface textures. As part of our work, we are releasing two-dimensional texture data: roughness and hardness to the research community.","PeriodicalId":265529,"journal":{"name":"2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130796504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2