2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)最新文献_第3页

An information theoretic source seeking strategy for plume tracking in 3D turbulent fields 三维湍流场羽流跟踪的信息论寻源策略

2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2015-10-01 DOI: 10.1109/SSRR.2015.7442994

Hadi Hajieghrary, Alex Fabregat Tomas, M. A. Hsieh

引用次数: 12

CAT 360 — Canine augmented technology 360-degree video system CAT 360-犬类增强技术360度视频系统

2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2015-10-01 DOI: 10.1109/SSRR.2015.7443003

A. Ferworn, Ben Waismark, Mike Scanlan

引用次数: 9

A man-packable unmanned surface vehicle for radiation localization and forensics 用于辐射定位和取证的可携带无人水面飞行器

2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2015-10-01 DOI: 10.1109/SSRR.2015.7442944

G. Wilde, R. Murphy, Dylan A. Shell, C. Marianno

{"title":"A man-packable unmanned surface vehicle for radiation localization and forensics","authors":"G. Wilde, R. Murphy, Dylan A. Shell, C. Marianno","doi":"10.1109/SSRR.2015.7442944","DOIUrl":"https://doi.org/10.1109/SSRR.2015.7442944","url":null,"abstract":"This paper describes the first known man-packable unmanned surface vehicle (USV) system designed for radiation localization, either for nuclear treaty verification or sampling after a nuclear incident. Currently, outdoor radiation sampling is done manually or by ground or aerial unmanned systems. An unmanned surface vehicle can operate in shallow nuclear facility cooling ponds and connected streams, rivers, and bays to sample without entering the facility and while maintaining network connectivity to report readings. The Challenge 1 USV is a Lutra 1.1 autonomous airboat modified to carry an UltraRadiacTM. The debris-free area to survey is determined by teleoperation, then the system autonomously generates and executes a novel spiral path and sampling policy. Challenge 1 was tested for proof-of-concept at Texas A&M Engineering Extension Service's Disaster City® with a cesium-137 source placed near one shore of a pond 72 meters from the deployment point. During this test, 775 radiation readings were taken over an area of 57.61 square meters in 18 minutes. These data were used by an inverse distance weighting interpolation algorithm to produce a visual heatmap.","PeriodicalId":357384,"journal":{"name":"2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)","volume":"435 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123376862","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: 5

Towards an autonomous water monitoring system with an unmanned aerial and surface vehicle team 与无人驾驶飞机和地面车辆团队一起开发自主水监测系统

2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2015-10-01 DOI: 10.1109/SSRR.2015.7443007

Jun Han Bae, E. Matson, B. Min

引用次数: 4

People in the weeds: Pedestrian detection goes off-road 杂草丛生的人:行人检测越野

2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2015-10-01 DOI: 10.1109/SSRR.2015.7442951

Trenton Tabor, Zachary A. Pezzementi, Carlos Vallespí, Carl K. Wellington

{"title":"People in the weeds: Pedestrian detection goes off-road","authors":"Trenton Tabor, Zachary A. Pezzementi, Carlos Vallespí, Carl K. Wellington","doi":"10.1109/SSRR.2015.7442951","DOIUrl":"https://doi.org/10.1109/SSRR.2015.7442951","url":null,"abstract":"Robotics offers a great opportunity to improve efficiency while also improving safety, but reliable detection of humans in off-road environments remains a key challenge. We present a person detector evaluation on a dataset collected from an autonomous tractor in an off-road environment representing challenging conditions with significant occlusion from weeds and branches as well as non-standing poses. We apply three image-only algorithms from urban pedestrian detection to better understand how well these approaches work in this domain. We evaluate the Aggregate Channel Features (ACF) and Deformable Parts Model (DPM) algorithms from the literature, as well as our own implementation of a Convolutional Neural Network (CNN). We show that the traditional performance metric used in the pedestrian detection literature is extremely sensitive to parameterization. When applied in domains like this one, where localization is challenging due to high background texture and occlusion, the choice of overlap threshold strongly affects measured performance. Using a permissive overlap threshold, we found that ACF, DPM, and CNN perform similarly overall in this domain, although they each have different failure modes.","PeriodicalId":357384,"journal":{"name":"2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115110673","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: 8

Monocular visual-inertial fusion with online initialization and camera-IMU calibration 带有在线初始化和相机- imu标定的单目视觉惯性融合

2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2015-10-01 DOI: 10.1109/SSRR.2015.7442952

Zhenfei Yang, S. Shen

引用次数: 13

LORCA: A high performance USV with applications to surveillance and monitoring LORCA:一种高性能无人潜航器，用于监视和监控

2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2015-10-01 DOI: 10.1109/SSRR.2015.7443020

J. Grenestedt, J. Keller, S. Larson, J. Patterson, J. Spletzer, Tyler Trephan

引用次数: 6

Gaussian processes with input-dependent noise variance for wireless signal strength-based localization 基于输入噪声方差的高斯过程无线信号强度定位

2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2015-10-01 DOI: 10.1109/SSRR.2015.7442993

Renato Miyagusuku, A. Yamashita, H. Asama

{"title":"Gaussian processes with input-dependent noise variance for wireless signal strength-based localization","authors":"Renato Miyagusuku, A. Yamashita, H. Asama","doi":"10.1109/SSRR.2015.7442993","DOIUrl":"https://doi.org/10.1109/SSRR.2015.7442993","url":null,"abstract":"Gaussian Processes have been previously used to model wireless signals strength for its use as sensory input for robot localization. The standard Gaussian Process formulation assumes that the outputs are corrupted by identically independently distributed Gaussian noise. Even though, in general, wireless signals strength do not have homogeneous noise variance. If enough data samples are collected, the noise variance in office-like environments is usually low. In such cases the noise assumption holds. Previous work has demonstrated the viability of wireless signal strength-based localization in such office-like environments. We intend to extend the applicability of these models to perform robot localization in search and rescue scenarios. In such environments, we expect wireless signals strength measurements to be corrupted with high heteroscedastic noise variance. To extend the applicability of previous approaches to these scenarios, we relax the assumption regarding output noise, by considering that the noise variance depends on the inputs. In this work, we describe how this can be done for the specific case of modeling wireless signal strength. Our results show how relaxing this assumption helps improve localization using a synthetic data set generated by artificially increasing noise variance of real data taken from tests performed on a standard office-like environment.","PeriodicalId":357384,"journal":{"name":"2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115605669","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: 11

Autonomous landing of rotary-wing aerial vehicles by image-based visual servoing in GPS-denied environments 无gps环境下基于图像视觉伺服的旋翼飞行器自主降落

2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2015-10-01 DOI: 10.1109/SSRR.2015.7443009

Rafik Mebarki, V. Lippiello, B. Siciliano

引用次数: 13

Mock-up of the exhaust shaft inspection by dexterous hexrotor at the DOE WIPP site 美国能源部WIPP现场灵巧六旋翼排气轴检测模型

2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2015-10-01 DOI: 10.1109/SSRR.2015.7443006

Guangying Jiang, R. Voyles, K. Sebesta, H. Greiner

引用次数: 4