2015 IEEE International Conference on Robotics and Automation (ICRA)最新文献

Balance control strategy for legged robots with compliant contacts 柔性接触足式机器人的平衡控制策略

2015 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2015-07-02 DOI: 10.1109/ICRA.2015.7139806

M. Azad, M. Mistry

引用次数: 13

Traversability analysis for mobile robots in outdoor environments: A semi-supervised learning approach based on 3D-lidar data 户外环境下移动机器人的可遍历性分析:基于3d激光雷达数据的半监督学习方法

2015 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2015-07-02 DOI: 10.1109/ICRA.2015.7139749

Benjamin Suger, B. Steder, Wolfram Burgard

引用次数: 81

Bayesian fusion using conditionally independent submaps for high resolution 2.5D mapping 使用条件独立子图的贝叶斯融合实现高分辨率2.5D映射

2015 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2015-07-02 DOI: 10.1109/ICRA.2015.7139668

Liye Sun, Teresa Vidal-Calleja, J. V. Miró

{"title":"Bayesian fusion using conditionally independent submaps for high resolution 2.5D mapping","authors":"Liye Sun, Teresa Vidal-Calleja, J. V. Miró","doi":"10.1109/ICRA.2015.7139668","DOIUrl":"https://doi.org/10.1109/ICRA.2015.7139668","url":null,"abstract":"Typically 2.5D maps provide a compact and efficient representation of the environment. When sensor data is obtained from multiple sets of noisy measurements at differing resolutions, the problem of compounding this information together to provide an effective and efficient means of mapping is not trivial, particularly as the size of the environment increases. In this paper, we propose a general framework for integrating heterogeneous sensor data to obtain large-scale 2.5D probabilistic maps. Gaussian Processes are used to generate a prior map that learns the spatial correlation between nearby points. Bayesian data fusion is then employed to update these prior maps with new measurements from distinct sensor modalities. In order to deal with large scale data, a novel submapping strategy is introduced to perform the fusion step efficiently in dealing with large covariance matrices. Submaps are first marginalised from the learned correlated prior and then updated based on the property of conditional independence. Most notably, the technique lends itself to generate accurate estimates at arbitrary resolutions and is able to handle varying noise from disparate sensor sources. The framework is applied to pipeline thickness mapping, with experimental results in fusing a high-resolution sensor and a low-resolution sensor showing the ability of the proposed technique to capture spatial correlations to come up with more accurate results when compared with a naïve fusion approach.","PeriodicalId":399819,"journal":{"name":"2015 IEEE International Conference on Robotics and Automation (ICRA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115866696","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: 13

An online method for tight-tolerance insertion tasks for string and rope 一种在线方法，用于绳和绳的紧公差插入任务

2015 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2015-05-26 DOI: 10.1109/ICRA.2015.7139532

Weifu Wang, D. Berenson, Devin J. Balkcom

{"title":"An online method for tight-tolerance insertion tasks for string and rope","authors":"Weifu Wang, D. Berenson, Devin J. Balkcom","doi":"10.1109/ICRA.2015.7139532","DOIUrl":"https://doi.org/10.1109/ICRA.2015.7139532","url":null,"abstract":"This paper presents a fast tight-tolerance threading technique for string and rope. Instead of relying on simulations of these deformable objects to plan a path or compute control actions, we control the movement of the string with a virtual magnetic vector field emanating from the narrow openings we wish to thread through. We compute an approximate Jacobian to move the tip of the string through the vector field and propose a method to promote alignment of the head of the string to the opening. We also propose a method for re-grasping the string based on the relationship between the string's configuration, the orientation of the opening, and direction of gravity. This re-grasping method in conjunction with our controller can be used to thread the string through a sequence of openings. We evaluated our method in simulation (with simulated sensor noise) and on the Da Vinci surgical robot. Our results suggest that our method is quite robust to errors in sensing, and is capable of real-world threading tasks with the da Vinci robot, where the diameter of the string (3.5mm) and opening (4.9mm) differ by only 1.4 mm.","PeriodicalId":399819,"journal":{"name":"2015 IEEE International Conference on Robotics and Automation (ICRA)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115238745","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: 49

Leveraging image-based prior in cross-season place recognition 利用基于图像的先验跨季节位置识别

2015 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2015-05-26 DOI: 10.1109/ICRA.2015.7139961

Masatoshi Ando, Yuuto Chokushi, Kanji Tanaka, Kentaro Yanagihara

{"title":"Leveraging image-based prior in cross-season place recognition","authors":"Masatoshi Ando, Yuuto Chokushi, Kanji Tanaka, Kentaro Yanagihara","doi":"10.1109/ICRA.2015.7139961","DOIUrl":"https://doi.org/10.1109/ICRA.2015.7139961","url":null,"abstract":"In this paper, we address the challenging problem of single-view cross-season place recognition. A new approach is proposed for compact discriminative scene descriptor that helps in coping with changes in appearance in the environment. We focus on a simple effective strategy that uses objects whose appearance remain the same across seasons as valid landmarks. Unlike popular bag-of-words (BoW) scene descriptors that rely on a library of vector quantized visual features, our descriptor is based on a library of raw image data (e.g., visual experience shared by colleague robots, publicly available photo collections from Google StreetView), and directly mines it to identify landmarks (i.e., image patches) that effectively explain an input query/database image. The discovered landmarks are then compactly described by their pose and shape (i.e., library image ID, and bounding boxes) and used as a compact discriminative scene descriptor for the input image. We collected a dataset of single-view images across seasons with annotated ground truth, and evaluated the effectiveness of our scene description framework by comparing its performance to that of previous BoW approaches, and by applying an advanced Naive Bayes Nearest neighbor (NBNN) image-to-class distance measure.","PeriodicalId":399819,"journal":{"name":"2015 IEEE International Conference on Robotics and Automation (ICRA)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115377179","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: 26

Augmenting physical state prediction through structured activity inference 通过结构化活动推理增强物理状态预测

2015 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2015-05-26 DOI: 10.1109/ICRA.2015.7139262

Nam N. Vo, A. Bobick

{"title":"Augmenting physical state prediction through structured activity inference","authors":"Nam N. Vo, A. Bobick","doi":"10.1109/ICRA.2015.7139262","DOIUrl":"https://doi.org/10.1109/ICRA.2015.7139262","url":null,"abstract":"We address the problem of predicting the physical state of a an agent performing a known activity. In particular we are interested in predicting human movement during complex composite activities. Our proposed framework combines a graphical model that extends the Sequential Interval Network (SIN) [1] for modeling global temporal structure of activities with a low level dynamic system for modeling the dynamics of the physical state. Specifically, two sets of new hidden state variables are added: one with respect to the temporal structure and one with respect to time. A mapping factor is defined to ensure these variables values remain consistent and hence allows fusing the two sources of information. We then derive an inference algorithm for computing the posterior densities of the hidden variables. The system can run in an on-line predictive mode to recognize on-going activity and make predictions arbitrarily far in the future during execution of the activity. Experiments illustrate that the long term prediction performance benefits from the knowledge about the temporal structure of the activity while short term prediction performance is improved by incorporating the dynamics of physical state.","PeriodicalId":399819,"journal":{"name":"2015 IEEE International Conference on Robotics and Automation (ICRA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117342007","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: 1

Image based optimisation without global consistency for constant time monocular visual SLAM 无全局一致性的基于图像的恒时单目SLAM优化

2015 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2015-05-26 DOI: 10.1109/ICRA.2015.7140011

Vincent Lui, T. Drummond

引用次数: 10

Toward ice-relative navigation of underwater robotic vehicles under moving sea ice: Experimental evaluation in the Arctic sea 移动海冰下水下机器人航行器的冰相关导航:在北极海域的实验评估

2015 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2015-05-26 DOI: 10.1109/ICRA.2015.7139392

C. McFarland, M. Jakuba, S. Suman, J. Kinsey, L. Whitcomb

引用次数: 21

Running beyond the bio-inspired regime 超越生物激励制度

2015 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2015-05-26 DOI: 10.1109/ICRA.2015.7139828

D. Haldane, R. Fearing

引用次数: 38

Symmetries and reduction for multi-agent control 多智能体控制的对称性与约简

2015 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2015-05-26 DOI: 10.1109/ICRA.2015.7139952

Ashley Nettleman, B. Goodwine

{"title":"Symmetries and reduction for multi-agent control","authors":"Ashley Nettleman, B. Goodwine","doi":"10.1109/ICRA.2015.7139952","DOIUrl":"https://doi.org/10.1109/ICRA.2015.7139952","url":null,"abstract":"This paper computes all the continuous point-transformation symmetries for a planar multi-agent dynamical control system. The relative configuration among the agents is often the most important aspect of this problem, and this paper presents a method to “factor out” the location and orientation on the plane of a multi-agent formation problem. This is useful in the case where only the relative positioning of the agents is of concern. The method to compute the symmetries and determine the reduced coordinates is well-established. The contributions of this paper are (1) computing the symmetries for a multi-agent system, (2) highlighting the utility of the reduction for the formation control application such as formation stability and (3) analyzing the symmetries to determine a relatively broad class of systems which have the same symmetries and hence identical coordinates for reduction. Although it has been addressed in various ways, essentially all methods to determine the stability properties of the formation control problem need the dynamics to be formulated on a reduced, relative space. Hence, stability (and stability-like notions such as that which follow from LaSalle's Principle) analyses will be the main beneficiary of this work.","PeriodicalId":399819,"journal":{"name":"2015 IEEE International Conference on Robotics and Automation (ICRA)","volume":"97 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120940930","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