2015 European Conference on Mobile Robots (ECMR)最新文献

R2D2 reloaded: Dynamic video projection on a mobile service robot R2D2重新加载:移动服务机器人上的动态视频投影

2015 European Conference on Mobile Robots (ECMR) Pub Date : 2015-11-12 DOI: 10.1109/ECMR.2015.7324177

Ronny Stricker, Steffen Müller, H. Groß

引用次数: 3

Bayesian sensor fusion for multi-platform landmines detection 多平台地雷探测贝叶斯传感器融合

2015 European Conference on Mobile Robots (ECMR) Pub Date : 2015-11-12 DOI: 10.1109/ECMR.2015.7324194

J. Prado, S. Filipe, Lino Marques

{"title":"Bayesian sensor fusion for multi-platform landmines detection","authors":"J. Prado, S. Filipe, Lino Marques","doi":"10.1109/ECMR.2015.7324194","DOIUrl":"https://doi.org/10.1109/ECMR.2015.7324194","url":null,"abstract":"This paper presents a novel sensor fusion model able to combine data from Ground Penetrating Radar (GPR) and Metal Detectors (MD) in order to classify landmines in a scanned floor area. Currently, no sensor detects landmines directly: a metal detector detects landmines' metal content and a ground penetrating radar detects dielectric discontinuities in the soil, that may be generated by a buried landmine. Fusing the information from different types of such sensors would improve the Probability of Detecting (PoD) landmines and decrease the rate of False Alarms (FAR). The current work describes a Bayesian decision level fusion which was found to decrease the FAR and improve the PoD when compared with data level and feature level fusion approaches. The classifier was tested using different sensors attached to different mobile platforms and after geo-referencing the acquired data, and training the proposed fusion classifier over a large experimental data set containing landmines and other objects, significant improvements both in the PoD and FAR were observed. The presented results are based on data acquired with an IDS GPR array and two Vallon MD arrays, in DOVO military facilities, near Leuven, Belgium, during July 2014.","PeriodicalId":142754,"journal":{"name":"2015 European Conference on Mobile Robots (ECMR)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134620775","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: 4

Approaches to time-dependent gas distribution modelling 随时间变化的气体分布建模方法

2015 European Conference on Mobile Robots (ECMR) Pub Date : 2015-11-12 DOI: 10.1109/ECMR.2015.7324215

S. Asadi, A. Lilienthal

引用次数: 3

Multi-target & multi-detector people tracker for mobile robots 用于移动机器人的多目标多探测器人员跟踪器

2015 European Conference on Mobile Robots (ECMR) Pub Date : 2015-11-12 DOI: 10.1109/ECMR.2015.7324223

Andreu Corominas Murtra, J. Pagès, Sammy Pfeiffer

引用次数: 4

An extension of GHMMs for environments with occlusions and automatic goal discovery for person trajectory prediction 在有遮挡的环境中扩展ghmm，并在人轨迹预测中自动发现目标

2015 European Conference on Mobile Robots (ECMR) Pub Date : 2015-11-12 DOI: 10.1109/ECMR.2015.7324187

Ignacio Pérez-Hurtado, J. Capitán, F. Caballero, L. Merino

引用次数: 5

Local region detector + CNN based landmarks for practical place recognition in changing environments 局部区域检测器+基于CNN的地标，在不断变化的环境中进行实际的地点识别

2015 European Conference on Mobile Robots (ECMR) Pub Date : 2015-11-12 DOI: 10.1109/ECMR.2015.7324051

Peer Neubert, P. Protzel

{"title":"Local region detector + CNN based landmarks for practical place recognition in changing environments","authors":"Peer Neubert, P. Protzel","doi":"10.1109/ECMR.2015.7324051","DOIUrl":"https://doi.org/10.1109/ECMR.2015.7324051","url":null,"abstract":"Visual place recognition is a mature field in mobile robotics research. Recognizing places in datasets covering traversals of hundreds or thousands of kilometres and accurate localization in small and medium size environments have been successfully demonstrated. However, for real world long term operation, visual place recognition has to face severe environmental appearance changes due to day-night cycles, seasonal or weather changes. Existing approaches for recognizing places in such changing environments provide solutions for matching images from the exact same viewpoint using powerful holistic descriptors, using less sophisticated holistic descriptors in combinations with images sequences, and/or pose strong requirements on training data to learn systematic appearance changes. In this paper, we present a novel, training free, single image matching procedure that builds upon local region detectors for powerful Convolutional Neural Network (CNN) based descriptors. It can be used with a broad range of local region detectors including keypoints, segmentation based approaches and object proposals. We propose a novel evaluation criterion for selection of an appropriate local region detector for changing environments and compare several available detectors. The scale space extrema detector known from the SIFT keypoint detector in combination with appropriate magnification factors performs best. We present preliminary results of the proposed image matching procedure with several region detectors on the challenging Nordland dataset on place recognition between different seasons and a dataset including severe viewpoint changes. The proposed method outperforms the best existing holistic method for place recognition in such changing environments and can additionally handle severe viewpoint changes. Additionally, the combination of the best performing detectors with superpixel based spatial image support shows promising results.","PeriodicalId":142754,"journal":{"name":"2015 European Conference on Mobile Robots (ECMR)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129111228","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: 35

Floor determination in the operation of a lift by a mobile guide robot 移动引导机器人在电梯运行中确定楼层

2015 European Conference on Mobile Robots (ECMR) Pub Date : 2015-11-12 DOI: 10.1109/ECMR.2015.7324180

O. McAree, J. Aitken, L. Boorman, David Cameron, A. Chua, Emily C. Collins, Samuel Fernando, J. Law, Uriel Martinez-Hernandez

引用次数: 5

Attitude stabilization control of an aerial manipulator using a quaternion-based backstepping approach 基于四元数的航空机械臂姿态稳定控制

2015 European Conference on Mobile Robots (ECMR) Pub Date : 2015-11-12 DOI: 10.1109/ECMR.2015.7324191

S. D. Lucia, Gian Diego Tipaldi, Wolfram Burgard

引用次数: 34

Retrieval of arbitrary 3D objects from robot observations 从机器人观测中检索任意三维物体

2015 European Conference on Mobile Robots (ECMR) Pub Date : 2015-11-12 DOI: 10.1109/ECMR.2015.7324203

Nils Bore, P. Jensfelt, John Folkesson

引用次数: 6

Approximate surface reconstruction and registration for RGB-D SLAM RGB-D SLAM的近似曲面重建与配准

2015 European Conference on Mobile Robots (ECMR) Pub Date : 2015-11-12 DOI: 10.1109/ECMR.2015.7324182

D. Holz, Sven Behnke

引用次数: 3