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% Encoding: UTF-8
@InProceedings{Dudek2000,
author = {Dudek, Gregory and Jugessur, Deeptiman},
title = {Robust place recognition using local appearance based methods},
booktitle = {Robotics and Automation, 2000. Proceedings. ICRA'00. IEEE International Conference on},
year = {2000},
volume = {2},
pages = {1030--1035},
organization = {IEEE},
crossref = {@inproceedings{engel2013semi, title={Semi-dense visual odometry for a monocular camera}, author={Engel, Jakob and Sturm, Jurgen and Cremers, Daniel}, booktitle={Proceedings of the IEEE international conference on computer vision}, pages={1449--1456}, year={2013} }},
owner = {zero},
timestamp = {2015.04.22},
}
@Article{Endres2014,
author = {Endres, Felix and Hess, Juergen and Sturm, Juergen and Cremers, Daniel and Burgard, Wolfram},
journal = {IEEE Transactions on Robotics},
title = {3-D Mapping With an RGB-D Camera},
year = {2014},
number = {1},
pages = {177--187},
volume = {30},
comment = {Rgb-d SLAM经典作品，Endres大大的，2012年的icra上已发过一次。},
crossref = {@inproceedings{engel2013semi, title={Semi-dense visual odometry for a monocular camera}, author={Engel, Jakob and Sturm, Jurgen and Cremers, Daniel}, booktitle={Proceedings of the IEEE international conference on computer vision}, pages={1449--1456}, year={2013} }},
file = {Published version:Endres2014.pdf:PDF},
groups = {[x:]},
keywords = {rgb-d slam, graph-based slam, important, rank5, qualityAssured},
owner = {GaoXiang},
qualityassured = {qualityAssured},
ranking = {rank5},
timestamp = {2014.04.19},
}
@Article{Adams2014,
author = {Adams, M. and Vo, B.-N. and Mahler, R. and Mullane, J.VOV},
journal = {IEEE Robotics Automation Magazine},
title = {SLAM Gets a PHD: New Concepts in Map Estimation},
year = {2014},
issn = {1070-9932},
number = {2},
pages = {26--37},
volume = {21},
comment = {使用PHD作为基本理论的SLAM，比较有新意，且是该方向经典的工作，直接继承�??????????2013年的RFS理论。},
file = {Published version:Adams2014.pdf:PDF},
groups = {y:3},
keywords = {rfs, phd, important, rank4, qualityAssured},
owner = {y},
qualityassured = {qualityAssured},
ranking = {rank4},
timestamp = {2014.08.24},
}
@Article{Agarwal2014,
author = {Agarwal, P. and Burgard, W. and Stachniss, C.},
title = {Survey of Geodetic Mapping Methods: Geodetic Approaches to Mapping and the Relationship to Graph-Based SLAM},
journal = {Robotics Automation Magazine, IEEE},
year = {2014},
volume = {21},
number = {3},
pages = {63-80},
month = {Sept},
doi = {10.1109/MRA.2014.2322282},
file = {Agarwal2014.pdf:Agarwal2014.pdf:PDF},
issn = {1070-9932},
keywords = {SLAM (robots), cartography, mobile robots, path planning, geodetic approach, geodetic mapping methods, graph-based SLAM, large-scale mapping process, map building, robot localization, simultaneous localization and mapping, Mapping, Mobile robots, Poles and towers, Simultaneous localization and mapping, Sparse matrices, qualityAssured, rank4},
owner = {x},
timestamp = {2015.10.16}
}
@InCollection{Agrawal2008,
author = {Agrawal, Motilal and Konolige, Kurt and Blas, MortenRufus},
title = {CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching},
booktitle = {Computer Vision--ECCV 2008},
publisher = {Springer Berlin Heidelberg},
year = {2008},
editor = {Forsyth, David and Torr, Philip and Zisserman, Andrew},
volume = {5305},
series = {Lecture Notes in Computer Science},
pages = {102--115},
isbn = {978-3-540-88692-1},
comment = {提出FAST特征的文章，写到fast特征�一般要引用本文。},
file = {Published version:Agrawal2008.pdf:PDF},
keywords = {rank1, qualityAssured},
language = {English},
owner = {y},
timestamp = {2014.08.28},
}
@Article{Aldoma2012,
author = {Aldoma, Aitor and Marton, Zoltan-Csaba and Tombari, Federico and Wohlkinger, Walter and Potthast, Christian and Zeisl, Bernhard and Rusu, Radu Bogdan and Gedikli, Suat and Vincze, Markus},
title = {Point Cloud Library},
journal = {IEEE Robotics \& Automation Magazine},
year = {2012},
volume = {1070},
number = {9932/12},
comment = {PCL},
owner = {x},
timestamp = {2014.12.09},
}
@Article{An2012,
Title = {Line Segment-Based Indoor Mapping with Salient Line Feature Extraction},
Author = {An, S. Y. and Kang, J. G. and Lee, L. K. and Oh, S. Y.},
Journal = {Advanced Robotics},
Year = {2012},
Number = {5-6},
Pages = {437--460},
Volume = {26},
File = {An2012.pdf:An2012.pdf:PDF},
ISSN = {0169-1864},
Keywords = {RBPF-SLAM line segment scan point clustering iterative end point fitting line association mobile robot slam representation algorithms},
Owner = {x},
Timestamp = {2014.10.19},
Type = {Journal Article}
}
@Article{Anand2012,
author = {Anand, Abhishek and Koppula, Hema Swetha and Joachims, Thorsten and Saxena, Ashutosh},
journal = {The International Journal of Robotics Research},
title = {Contextually guided semantic labeling and search for three-dimensional point clouds},
year = {2012},
pages = {0278364912461538},
file = {Anand2012.pdf:Anand2012.pdf:PDF},
groups = {[x:]},
keywords = {qualityAssured, rank5},
owner = {x},
publisher = {SAGE Publications},
qualityassured = {qualityAssured},
ranking = {rank5},
timestamp = {2015.05.30},
}
@Article{Arbelaez2011,
Title = {Contour detection and hierarchical image segmentation},
Author = {Arbelaez, Pablo and Maire, Michael and Fowlkes, Charless and Malik, Jitendra},
Journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
Year = {2011},
Number = {5},
Pages = {898--916},
Volume = {33},
Owner = {x},
Publisher = {IEEE},
Timestamp = {2015.05.30}
}
@Article{Arth2015,
author = {Arth, C. and Pirchheim, C. and Ventura, J. and Schmalstieg, D. and Lepetit, V.},
title = {Instant Outdoor Localization and SLAM Initialization from 2.5D Maps},
journal = {Visualization and Computer Graphics, IEEE Transactions on},
year = {2015},
volume = {21},
number = {11},
pages = {1309-1318},
month = {Nov},
doi = {10.1109/TVCG.2015.2459772},
file = {Arth2015.pdf:Arth2015.pdf:PDF},
issn = {1077-2626},
keywords = {Buildings, Cameras, Image segmentation, Mobile handsets, Simultaneous localization and mapping, Solid modeling, Three-dimensional displays, 2D map, SLAM, geo-localization, image registration, outdoor augmented reality, qualityAssured, rank1},
owner = {x},
timestamp = {2015.10.16}
}
@InProceedings{Arthur2007,
author = {Arthur, David and Vassilvitskii, Sergei},
title = {K-means++: The advantages of careful seeding},
booktitle = {Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms},
year = {2007},
pages = {1027--1035},
organization = {Society for Industrial and Applied Mathematics},
owner = {cyang},
timestamp = {2016.10.01},
}
@Article{Artieda2009,
author = {Artieda, Jorge and Sebastian, Jos{\'e} M and Campoy, Pascual and Correa, Juan F and Mondrag{\'o}n, Iv{\'a}n F and Mart{\'\i}nez, Carol and Olivares, Miguel},
title = {Visual 3-d slam from uavs},
journal = {Journal of Intelligent and Robotic Systems},
year = {2009},
volume = {55},
number = {4-5},
pages = {299--321},
comment = {SLAM在UAV里的应用},
owner = {x},
publisher = {Springer},
timestamp = {2015.05.17},
}
@Article{Arun1987,
author = {Arun, K Somani and Huang, Thomas S and Blostein, Steven D},
title = {Least-squares fitting of two 3-D point sets},
journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
year = {1987},
number = {5},
pages = {698--700},
comment = {ICP初始文章。写ICP时引用�?�},
keywords = {rank1, qualityAssured},
owner = {x},
publisher = {IEEE},
timestamp = {2014.09.28},
}
@Article{Bacca2013,
author = {B. Bacca and J. Salvi and X. Cuf{\'{\i}}},
journal = {Robotics and Autonomous Systems},
title = {Long-term mapping and localization using feature stability histograms},
year = {2013},
issn = {0921-8890},
number = {12},
pages = {1539--1558},
volume = {61},
abstract = {Abstract This work proposes a system for long-term mapping and localization based on the Feature Stability Histogram (FSH) model which is an innovative feature management approach able to cope with changing environments. \{FSH\} is built using a voting schema, where re-observed features are promoted; otherwise the feature progressively decreases its corresponding \{FSH\} value. \{FSH\} is inspired by the human memory model. This model introduces concepts of Short-Term Memory (STM), which retains information long enough to use it, and Long-Term Memory (LTM), which retains information for longer periods of time. If the entries in \{STM\} are continuously rehearsed, they become part of LTM. However, this work proposes a change in the pipeline of this model, allowing any feature to be part of \{STM\} or \{LTM\} depending on the feature strength. \{FSH\} stores the stability values of local features, stable features are only used for localization and mapping. Experimental validation of the \{FSH\} model was conducted using the FastSLAM framework and a long-term dataset collected during a period of one year at different environmental conditions. The experiments carried out include qualitative and quantitative results such as: filtering out dynamic objects, increasing map accuracy, scalability, and reducing the data association effort in long-term runs.},
file = {Published version:Bacca2013.pdf:PDF},
keywords = {Long-term localization and mapping},
owner = {y},
timestamp = {2014.08.25},
}
@Article{Bachrach2012,
author = {Bachrach, Abraham and Prentice, Samuel and He, Ruijie and Henry, Peter and Huang, Albert S and Krainin, Michael and Maturana, Daniel and Fox, Dieter and Roy, Nicholas},
journal = {The International Journal of Robotics Research},
title = {Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments},
year = {2012},
number = {11},
pages = {1320--1343},
volume = {31},
comment = {大�?�全，从SLAM到Planning的所有东西都提到了�?? 用了Fast特征点，金字塔提取�?? IJRR上的都是这种巨大的文章么�???????????? -从现在看过的而言确实是啊。顶楼主。},
file = {Published version:Bachrach2012.pdf:PDF},
groups = {y:5},
keywords = {rgb-d slam, planning, important, rank3, qualityAssured},
owner = {y},
publisher = {Sage Publications},
qualityassured = {qualityAssured},
ranking = {rank3},
timestamp = {2014.06.11},
}
@Article{Balzer2013,
Title = {CLAM: Coupled Localization and Mapping with Efficient Outlier Handling},
Author = {Balzer, J. and Soatto, S.},
Journal = {2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
Year = {2013},
Pages = {1554--61},
File = {Published version:Balzer2013.pdf:PDF},
Owner = {GaoXiang},
Timestamp = {2014.01.13}
}
@Misc{Barfoot2016,
author = {Barfoot, TD},
title = {State Estimation for Robotics: A Matrix Lie Group Approach},
year = {2016},
publisher = {Draft in preparation for publication by Cambridge University Press},
}
@Article{Barkby2012,
Title = {Bathymetric particle filter SLAM using trajectory maps},
Author = {Barkby, S. and Williams, S. B. and Pizarro, O. and Jakuba, M. V.},
Journal = {International Journal of Robotics Research},
Year = {2012},
Note = {Times Cited: 5 Barkby, Stephen Williams, Stefan B. Pizarro, Oscar Jakuba, Michael V. 5 Si},
Number = {12},
Pages = {1409--1430},
Volume = {31},
Doi = {10.1177/0278364912459666},
ISSN = {0278-3649},
Keywords = {SLAM mapping navigation bathymetry Gaussian process RBPF vehicles},
Owner = {x},
Timestamp = {2014.10.19},
Type = {Journal Article},
Url = {<Go to ISI>://WOS:000311643300005}
}
@Article{Bastien2012,
Title = {Theano: new features and speed improvements},
Author = {Bastien, Fr{\'e}d{\'e}ric and Lamblin, Pascal and Pascanu, Razvan and Bergstra, James and Goodfellow, Ian and Bergeron, Arnaud and Bouchard, Nicolas and Warde-Farley, David and Bengio, Yoshua},
Journal = {arXiv preprint arXiv:1211.5590},
Year = {2012},
Owner = {zero},
Timestamp = {2015.04.12}
}
@InCollection{Bay2006,
author = {Bay, Herbert and Tuytelaars, Tinne and Van Gool, Luc},
title = {Surf: Speeded up robust features},
booktitle = {Computer Vision--ECCV 2006},
publisher = {Springer},
year = {2006},
pages = {404--417},
comment = {提出surf的文章�?�},
keywords = {SURF, rank1, qualityAssured},
owner = {x},
timestamp = {2014.09.30},
}
@Article{Beeson2010,
author = {Beeson, P. and Modayil, J. and Kuipers, B.},
title = {Factoring the Mapping Problem: Mobile Robot Map-building in the Hybrid Spatial Semantic Hierarchy},
journal = {International Journal of Robotics Research},
year = {2010},
volume = {29},
number = {4},
pages = {428--459},
issn = {0278-3649},
comment = {14.10.26 极其牛叉的一篇文章�?�讲述了建图的方方面面，综述写的十分精彩�? 整篇长文是围绕HSSH（Hybrid Spatial Semantic Hierachy）展�?的，其核心�?�想是，�?部地图用Metric表示，全�?地图用拓扑表示�?�Metric表示比较精确，可以用于准确的定点与导航，但是范围小，容易出现累积误差。拓扑地图则对于距离误差不敏感，适合表示大范围的地图。对于拓扑地图，该文使用了一套完整的符号，包括Path, Region, Place, Gateway。简单地说，地图是由若干个Region通过Gateway连接起来的东西�?�},
file = {Beeson2010.pdf:pdf/Beeson2010.pdf:PDF},
keywords = {mapping localization autonomous agents cognitive robotics topological maps localization representation environments complexity inference space slam, rank5, qualityAssured},
owner = {x},
timestamp = {2014.10.19},
type = {Journal Article},
}
@Article{Benedettelli2012,
author = {Benedettelli, D. and Garulli, A. and Giannitrapani, A.},
title = {Cooperative SLAM using M-Space representation of linear features},
journal = {Robotics and Autonomous Systems},
year = {2012},
volume = {60},
number = {10},
pages = {1267--1278},
issn = {0921-8890},
comment = {14.10.27 只看了摘要�?? 讲多机器人SLAM的文章�?? 分为三步�????????????1.各机器人独立进行SLAM，生成独立的地图�????????????2.当机器人相遇（meet）时，合并子图；3.之后的SLAM过程在合并之后的地图上进行�?? 个人猜测：多机器人的相对位姿的配准时机比较讲究，不能随时随地就配。所以要设计出这么一套机制�?�},
keywords = {SLAM Mapping Multi-robot M-Space simultaneous localization environments, rank1, qualityAssured},
owner = {x},
timestamp = {2014.10.19},
type = {Journal Article},
}
@Article{Bengio2013,
author = {Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
title = {Representation learning: A review and new perspectives},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2013},
volume = {35},
number = {8},
pages = {1798--1828},
comment = {15.1.13 和bengio以往的作品差不多，讲关于dl各种算法的发展进度和应用�????????????30多页的一篇综述�?? 写得非常全面，在引用dl文献时可参照这篇文章去引。},
file = {Bengio2013.pdf:Bengio2013.pdf:PDF},
keywords = {qualityAssured, rank5},
owner = {x},
publisher = {IEEE},
timestamp = {2014.12.17},
}
@Article{Bentley1975,
author = {Bentley, Jon Louis},
title = {Multidimensional binary search trees used for associative searching},
journal = {Communications of the ACM},
year = {1975},
volume = {18},
number = {9},
pages = {509--517},
owner = {cyang},
publisher = {ACM},
timestamp = {2016.10.02},
}
@InProceedings{Bergstra2010,
author = {Bergstra, James and Breuleux, Olivier and Bastien, Fr{\'{e}}d{\'{e}}ric and Lamblin, Pascal and Pascanu, Razvan and Desjardins, Guillaume and Turian, Joseph and Warde-Farley, David and Bengio, Yoshua},
title = {Theano: a {CPU} and {GPU} Math Expression Compiler},
booktitle = {Proceedings of the Python for Scientific Computing Conference ({SciPy})},
year = {2010},
month = {\#jun\#},
note = {Oral Presentation},
abstract = {Theano is a compiler for mathematical expressions in Python that combines the convenience of NumPy’s syntax with the speed of optimized native machine language. The user composes mathematical expressions in a high-level description that mimics NumPy’s syntax and semantics, while being statically typed and functional (as opposed to imperative). These expressions allow Theano to provide symbolic differentiation. Before performing computation, Theano optimizes the choice of expressions, translates them into C++ (or CUDA for GPU), compiles them into dynamically loaded Python modules, all automatically. Common machine learning algorithms implemented with Theano are from 1.6�???????????? to 7.5�???????????? faster than competitive alternatives (including those implemented with C/C++, NumPy/SciPy and MATLAB) when compiled for the CPU and between 6.5�???????????? and 44�???????????? faster when compiled for the GPU. This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design.},
comment = {theano},
location = {Austin, TX},
owner = {x},
timestamp = {2015.01.05},
}
@InProceedings{Birem2014,
Title = {SAIL-MAP: Loop-closure detection using saliency-based features},
Author = {Birem, Merwan and Quinton, Jean-Charles and Berry, Francois and Mezouar, Youcef},
Booktitle = {Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on},
Year = {2014},
Organization = {IEEE},
Pages = {4543--4548},
Owner = {x},
Timestamp = {2015.01.01}
}
@Article{Biswas2013,
Title = {Localization and navigation of the CoBots over long-term deployments},
Author = {Biswas, Joydeep and Veloso, Manuela M.},
Journal = {The International Journal of Robotics Research},
Year = {2013},
Number = {14},
Pages = {1679--1694},
Volume = {32},
Abstract = {For the last three years, we have developed and researched multiple collaborative robots, CoBots, which have been autonomously traversing our multi-floor buildings. We pursue the goal of long-term autonomy for indoor service mobile robots as the ability for them to be deployed indefinitely while they perform tasks in an evolving environment. The CoBots include several levels of autonomy, and in this paper we focus on their localization and navigation algorithms. We present the Corrective Gradient Refinement (CGR) algorithm, which refines the proposal distribution of the particle filter used for localization with sensor observations using analytically computed state space derivatives on a vector map. We also present the Fast Sampling Plane Filtering algorithm that extracts planar regions from depth images in real time. These planar regions are then projected onto the 2D vector map of the building, and along with the laser rangefinder observations, used with CGR for localization. For navigation, we present a hierarchical planner, which computes a topological policy using a graph representation of the environment, computes motion commands based on the topological policy, and then modifies the motion commands to side-step perceived obstacles. We started logging the deployments of the CoBots one and a half years ago, and have since collected logs of the CoBots traversing more than 130 km over 1082 deployments and a total run time of 182 h, which we publish as a dataset consisting of more than 10 million laser scans. The logs show that although there have been continuous changes in the environment, the robots are robust to most of them, and there exist only a few locations where changes in the environment cause increased uncertainty in localization.},
Eprint = {http://ijr.sagepub.com/cgi/reprint/32/14/1679},
File = {Published version:Biswas2013.pdf:PDF},
Owner = {y},
Timestamp = {2014.08.24}
}
@Article{Blanco2013,
Title = {A robust, multi-hypothesis approach to matching occupancy grid maps},
Author = {Blanco, J. L. and Gonzalez-Jimenez, J. and Fernandez-Madrigal, J. A.},
Journal = {Robotica},
Year = {2013},
Pages = {687--701},
Volume = {31},
ISSN = {0263-5747},
Keywords = {Mobile robots SLAM Robot localization Pose estimation and registration navigation metric-topological slam image registration local descriptors representation environments consensus robots},
Owner = {x},
Timestamp = {2014.10.19},
Type = {Journal Article}
}
@Article{Bo2014,
author = {Bo, Liefeng and Ren, Xiaofeng and Fox, Dieter},
journal = {International Journal of Robotics Research},
title = {Learning hierarchical sparse features for RGB-D object recognition},
year = {2014},
number = {4},
pages = {581--599},
volume = {33},
comment = {14.11.18 用层次化�????????????疏编码（怎么�????????????么都可以层次化）来学习RGBD数据中的特征。用到了R,G,B,I,D和法线共八维的向量�?? 方法是先用K-SVD学习字典，然后用OMP（正交追踪）计算在当前字典下的编码�?�我觉得该工作也可以用于Loop Closure Dection中�?? 优点：学习字典与线�?�变换，计算量相对较小�?? 缺点：线性方法有�????????????限�?�；把图像分成batches可能丢失了batch之间的结构信息�?? 15.1.13 这篇文章值得细读�????????????读�?�},
file = {Published version:Bo2014.pdf:PDF},
groups = {[x:]},
keywords = {object recognition, feature, sparse coding, Feature learning, rank5, qualityAssured},
owner = {y},
publisher = {SAGE Publications},
qualityassured = {qualityAssured},
ranking = {rank5},
timestamp = {2014.08.24},
}
@Article{Boal2014,
author = {Boal,Jaime and S{\'{a}}nchez-Miralles,{\'{A}}lvaro and Arranz,{\'{A}}lvaro},
title = {Topological simultaneous localization and mapping: a survey},
journal = {Robotica},
year = {2014},
volume = {32},
pages = {803--821},
abstract = {ABSTRACT SUMMARY One of the main challenges in robotics is navigating autonomously through large, unknown, and unstructured environments. Simultaneous localization and mapping (SLAM) is currently regarded as a viable solution for this problem. As the traditional metric approach to SLAM is experiencing computational difficulties when exploring large areas, increasing attention is being paid to topological SLAM, which is bound to provide sufficiently accurate location estimates, while being significantly less computationally demanding. This paper intends to provide an introductory overview of the most prominent techniques that have been applied to topological SLAM in terms of feature detection, map matching, and map fusion.},
file = {Boal2014.pdf:Boal2014.pdf:PDF},
issn = {1469-8668},
issue = {05},
numpages = {19},
owner = {y},
timestamp = {2014.08.25},
}
@InProceedings{Bosse2003,
Title = {An Atlas framework for scalable mapping},
Author = {Bosse, Michael and Newman, Paul and Leonard, John and Soika, Martin and Feiten, Wendelin and Teller, Seth},
Booktitle = {Robotics and Automation, 2003. Proceedings. ICRA'03. IEEE International Conference on},
Year = {2003},
Organization = {IEEE},
Pages = {1899--1906},
Volume = {2},
Owner = {x},
Timestamp = {2015.05.18}
}
@Article{Botterill2011,
Title = {Bag-of-Words-Driven, Single-Camera Simultaneous Localization and Mapping},
Author = {Botterill, T. and Mills, S. and Green, R.},
Journal = {Journal of Field Robotics},
Year = {2011},
Number = {2},
Pages = {204--226},
Volume = {28},
ISSN = {1556-4959},
Keywords = {appearance algorithm vision graphs slam map},
Owner = {x},
Timestamp = {2014.10.19},
Type = {Journal Article}
}
@Article{Botterill2013,
Title = {Correcting Scale Drift by Object Recognition in Single-Camera SLAM},
Author = {Botterill, Tom and Mills, Steven and Green, Richard},
Journal = {IEEE Transactions On Cybernetics},
Year = {2013},
Number = {6SI},
Pages = {1767--1780},
Volume = {43},
File = {Published version:Botterill2013.pdf:PDF},
Owner = {GaoXiang},
Timestamp = {2014.01.13}
}
@Article{Bourlard1988,
Title = {Auto-association by multilayer perceptrons and singular value decomposition},
Author = {Bourlard, Herv{\'e} and Kamp, Yves},
Journal = {Biological cybernetics},
Year = {1988},
Number = {4-5},
Pages = {291--294},
Volume = {59},
Owner = {zero},
Publisher = {Springer},
Timestamp = {2015.04.09}
}
@Article{Bradski2000,
author = {Bradski, Gary},
title = {The opencv library},
journal = {Doctor Dobbs Journal},
year = {2000},
volume = {25},
number = {11},
pages = {120--126},
comment = {opencv引用},
owner = {x},
publisher = {M AND T PUBLISHING INC},
timestamp = {2014.12.09},
}
@Article{Bresson2015,
author = {Bresson, G. and Feraud, T. and Aufrere, R. and Checchin, P. and Chapuis, R.},
title = {Real-Time Monocular SLAM With Low Memory Requirements},
journal = {Intelligent Transportation Systems, IEEE Transactions on},
year = {2015},
volume = {16},
number = {4},
pages = {1827-1839},
month = {Aug},
doi = {10.1109/TITS.2014.2376780},
file = {Bresson2015.pdf:Bresson2015.pdf:PDF},
issn = {1524-9050},
keywords = {Kalman filters, SLAM (robots), covariance matrices, linearisation techniques, mobile robots, position control, robot vision, 3-D uncertainty, EKF-SLAM algorithm, Kalman gain, MSLAM, corrective factor, covariance matrix, extended Kalman filter, image plane, linearization errors, linearization failures, low memory requirements, minimal Cartesian representation, monocular SLAM problem, real-time monocular SLAM, simultaneous localization and mapping techniques, unknown environment, vehicle localization, Cameras, Ellipsoids, Jacobian matrices, Kalman filters, Simultaneous localization and mapping, Uncertainty, Vehicles, Intelligent vehicles, land vehicles, robot vision systems, simultaneous localization and mapping, qualityAssured, rank2},
owner = {x},
timestamp = {2015.10.16}
}
@InProceedings{Burri2015,
author = {Burri, Michael and Oleynikova, Helen and Achtelik, Markus W and Siegwart, Roland},
title = {Real-time visual-inertial mapping, re-localization and planning onboard MAVs in unknown environments},
booktitle = {Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on},
year = {2015},
pages = {1872--1878},
organization = {IEEE},
}
@Article{Cadena2012,
author = {Cadena, C. and Galvez-Lopez, D. and Tardos, J. D. and Neira, J.},
title = {Robust Place Recognition With Stereo Sequences},
journal = {IEEE Transactions on Robotics},
year = {2012},
volume = {28},
number = {4},
pages = {871--885},
issn = {1552-3098},
comment = {14.10.28 主题是用BoW模型做loop closure detection。在传统的BoW上面做了�????????????些改进，例如引入归一化的相似度�?�评判相似度之后做了CRF�????????????测�?? 使用surf作为图像特征�???????????? 验证工作十分完善。与FAB-MAP 2.0比较了precision和recall曲线�???????????? 15.1.13 可作为不错的参�?�文献，看看研究lc的同学都用什么指标来评价算法的�?�},
file = {Cadena2012.pdf:pdf/Cadena2012.pdf:PDF},
keywords = {Bag of words (BoW) computer vision conditional random fields (CRFs) recognition simultaneous localization and mapping (SLAM) random-fields localization, rank4, qualityAssured},
owner = {x},
timestamp = {2014.10.19},
type = {Journal Article},
}
@Article{Cadena2014,
author = {Cadena, César and Košecká, Jana},
journal = {The International Journal of Robotics Research},
title = {Semantic parsing for priming object detection in indoors RGB-D scenes},
year = {2014},
abstract = {The semantic mapping of the environment requires simultaneous segmentation and categorization of the acquired stream of sensory information. The existing methods typically consider the semantic mapping as the final goal and differ in the number and types of considered semantic categories. We envision semantic understanding of the environment as an on-going process and seek representations which can be refined and adapted depending on the task and robot’s interaction with the environment. In this work we propose a novel and efficient method for semantic parsing, which can be adapted to the task at hand and enables localization of objects of interest in indoor environments. For basic mobility tasks we demonstrate how to obtain initial semantic segmentation of the scene into ground, structure, furniture and props categories which constitute the first level of hierarchy. Then, we propose a simple and efficient method for predicting locations of objects that based on their size afford a manipulation task. In our experiments we use the publicly available NYU V2 dataset and obtain better or comparable results than the state of the art at a fraction of the computational cost. We show the generalization of our approach on two more publicly available datasets.},
comment = {cadena出品，必属精品系列。 思路是先把地图用super pixel，弄出一个生成树。然后对每一个super pixel，提取一共十五维的特征，包括外观，3D信息，用它们来分类。 事实上语义地图就是个分割-分类的问题，难点是要做到实时，并且类别设置要妥当。 本文的类别是四分类：ground, structure, furniture, props. 也就是所有的super pixel都属于这四类之一。 对于不同的任务，其对于地图的认识层次需求也是不用的。机器人只需感知有兴趣的物体，不用识别环境里所有乱七八糟的东西。 文章有代码：www.di.ens.fr/~mschmidt/Software/UGM.html dataset: NYU V2 2015.6 仔仔细细地读了一遍，代码也跑通了，虽然未细看。 核心是用CRF表示类别的分布，并推理出每个物体的标签。 流程：super pixel seg. -> image & 3d features ->graph structure -> compute potentials of CRF -> inference. 对一张普通的图需要计算20秒左右。可能是因为matlab本身慢。最花时间的是3D特征的计算，尽管论文里认为是entropy。 优点：CRF可以推理出很复杂的结构。灵活性好。 缺点：仅能对单张图进行表示，计算量仍然很大（尽管树结构能简化图的表达），整体算法较复杂。需要事先训练分类器。语义方面只有四个类别，比较单一。 最直观的应用是直接丢进SLAM里计算，这样能得到一张semantic map，真正的语义地图，虽然语义比较简单。这个在Zhao2014中已经做过了。},
eprint = {http://ijr.sagepub.com/content/early/2014/10/27/0278364914549488.full.pdf+html},
file = {Cadena2014.pdf:Cadena2014.pdf:PDF},
groups = {[x:]},
keywords = {qualityAssured, rank5},
owner = {x},
qualityassured = {qualityAssured},
ranking = {rank5},
timestamp = {2015.01.01},
}
@Article{Cadena2016,
author = {Cesar, Cadena and Luca Carlone and Henry C. and Yasir Latif and Davide Scaramuzza and Jose Neira and Ian D Reid and John J., Leonard},
title = {Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age},
journal = {arXiv preprint arXiv:1606.05830},
year = {2016},
file = {Cadena2016.pdf:Cadena2016.pdf:PDF},
}
@inproceedings{calonder2010brief,
title={Brief: Binary robust independent elementary features},
author={Calonder, Michael and Lepetit, Vincent and Strecha, Christoph and Fua, Pascal},
booktitle={European conference on computer vision},
pages={778--792},
year={2010},
organization={Springer}
}
@Article{Carlevaris-Bianco2014,
author = {Carlevaris-Bianco, N. and Kaess, M. and Eustice, R.M.},
title = {Generic Node Removal for Factor-Graph SLAM},
journal = {Robotics, IEEE Transactions on},
year = {2014},
volume = {30},
number = {6},
pages = {1371-1385},
month = {Dec},
doi = {10.1109/TRO.2014.2347571},
file = {Carlevaris-Bianco2014.pdf:Carlevaris-Bianco2014.pdf:PDF},
issn = {1552-3098},
keywords = {SLAM (robots), computational complexity, graph theory, mobile robots, GLC method, Kullback-Leibler divergence, computational complexity, factor-graph SLAM, generic factor-based method, generic linear constraints, generic node removal, monocular vision, simultaneous localization and mapping, Approximation methods, Correlation, Mobile robots, Optimization, Simultaneous localization and mapping, Factor-graphs, long-term autonomy, marginalization, mobile robotics, simultaneous localization and mapping (SLAM), qualityAssured, rank3},
owner = {x},
timestamp = {2015.10.16}
}
@Article{Carlone2011,
Title = {Simultaneous Localization and Mapping Using {Rao-Blackwell}ized Particle Filters in Multi Robot Systems},
Author = {Carlone, L. and Ng, M. K. and Du, J. J. and Bona, B. and Indri, M.},
Journal = {Journal of Intelligent \& Robotic Systems},
Year = {2011},
Number = {2},
Pages = {283--307},
Volume = {63},
ISSN = {0921-0296},
Keywords = {Mobile robots Multi robot SLAM Rao-Blackwellized particle filters slam},
Owner = {x},
Timestamp = {2014.10.19},
Type = {Journal Article}
}
@Article{Carlone2014,
Title = {From Angular Manifolds to the Integer Lattice: Guaranteed Orientation Estimation With Application to Pose Graph Optimization},
Author = {Carlone, L. and Censi, A.},
Journal = {Robotics, IEEE Transactions on},
Year = {2014},
Month = {April},
Number = {2},
Pages = {475-492},
Volume = {30},
Doi = {10.1109/TRO.2013.2291626},
ISSN = {1552-3098},
Keywords = {computational complexity;concave programming;graph theory;integer programming;iterative methods;maximum likelihood estimation;probability;quadratic programming;statistical analysis;MOIE2D;angular manifolds;angular pose component;guaranteed orientation estimation;integer lattice;iterative pose graph optimization methods;iterative solvers;likelihood function;manifold product;maximum likelihood estimate;multihypothesis orientation-from-lattice estimation in 2D;node orientation;nonlinear optimization problem;nontrivial topology;precise probabilistic guarantees;unconstrained quadratic optimization problem;Manifolds;Maximum likelihood estimation;Noise;Optimization;Simultaneous localization and mapping;Integer quadratic programming;SO(2) manifold;mobile robots;multi-hypothesis estimation;orientation estimation;pose graph optimization;simultaneous localization and mapping (SLAM)},
Owner = {x},
Timestamp = {2015.10.16}
}
@InProceedings{Carrera2011,
Title = {SLAM-based automatic extrinsic calibration of a multi-camera rig},
Author = {Carrera, Gerardo and Angeli, Adrien and Davison, Andrew J},
Booktitle = {Robotics and Automation (ICRA), 2011 IEEE International Conference on},
Year = {2011},
Organization = {IEEE},
Pages = {2652--2659},
Owner = {x},
Timestamp = {2015.05.18}
}
@Article{Castellanos2001,
author = {Castellanos, JA and Neira, J and Tardos, JD},
title = {Multisensor fusion for simultaneous localization and map building},
journal = {IEEE Transactions On Robotics And Automation},
year = {2001},
volume = {17},
number = {6},
pages = {908--914},
month = {\#dec\#},
issn = {{1042-296X}},
abstract = {This paper describes how multisensor fusion increases both reliability and precision of the environmental observations used for the simultaneous localization and map-building problem for mobile robots. Multisensor fusion is performed at the level of landmarks, which represent sets of related and possibly correlated sensor observations. The work emphasizes the idea of partial redundancy due to the different nature of the information provided by different sensors. Experimentation with a mobile robot equipped with a multisensor system composed of a 2-D laser rangefinder and a charge coupled device camera is reported.},
comment = {早期的sensor fusion文章。},
file = {Castellanos2001.pdf:Castellanos2001.pdf:PDF},
keywords = {application, indoor, rank1, qualityAssured},
orcid-numbers = {{Tardos, Juan/0000-0002-4518-5876}},
owner = {x},
researcherid-numbers = {{Tardos, Juan/F-9204-2013}},
timestamp = {2014.10.05},
unique-id = {{ISI:000173337600014}},
}
@Article{Castle2010,
Title = {Combining monoSLAM with object recognition for scene augmentation using a wearable camera},
Author = {Castle, R. O. and Klein, G. and Murray, D. W.},
Journal = {Image And Vision Computing},
Year = {2010},
Number = {11},
Pages = {1548--1556},
Volume = {28},
File = {Published version:Castle2010.pdf:PDF},
Owner = {GaoXiang},
Timestamp = {2014.01.13}
}
@Article{Cheein2010,
Title = {SLAM algorithm applied to robotics assistance for navigation in unknown environments},
Author = {Cheein, Fernando A Auat and Lopez, Natalia and Soria, Carlos M and di Sciascio, Fernando A and Pereira, F Lobo and Carelli, Ricardo},
Journal = {Journal of neuroengineering and rehabilitation},
Year = {2010},
Number = {1},
Pages = {10},
Volume = {7},
Owner = {x},
Publisher = {BioMed Central Ltd},
Timestamp = {2015.05.17}
}
@InProceedings{Chekhlov2007,
author = {Chekhlov, Denis and Gee, Andrew P and Calway, Andrew and Mayol-Cuevas, Walterio},
title = {Ninja on a plane: Automatic discovery of physical planes for augmented reality using visual slam},
booktitle = {Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality},
year = {2007},
pages = {1--4},
organization = {IEEE Computer Society},
comment = {SLAM在VR里的应用},
owner = {x},
timestamp = {2015.05.17},
}
@Article{Chen2007,
author = {Chen, Zhenhe and Samarabandu, Jagath and Rodrigo, Ranga},
journal = {Advanced Robotics},
title = {Recent advances in simultaneous localization and map-building using computer vision},
year = {2007},
number = {3-4},
pages = {233--265},
volume = {21},
comment = {比较经典的视觉SLAM综述，可惜比较早，后面的工作没有讲到。},
file = {Published version:Chen2007.pdf:PDF},
groups = {[x:]},
keywords = {review, vSLAM, rank4, qualityAssured},
owner = {y},
publisher = {Taylor \& Francis},
qualityassured = {qualityAssured},
ranking = {rank4},
timestamp = {2014.08.24},
}
@Article{Chen2012,
author = {Chen, S. Y.},
title = {Kalman Filter for Robot Vision: A Survey},
journal = {IEEE Transactions on Industrial Electronics},
year = {2012},
volume = {59},
number = {11},
pages = {4409--4420},
file = {Published version:Chen2012.pdf:PDF},
keywords = {EKF, survey, rank3},
owner = {GaoXiang},
timestamp = {2014.01.13}
}
@InProceedings{Cheng2014,
Title = {BING: Binarized normed gradients for objectness estimation at 300fps},
Author = {Cheng, Ming-Ming and Zhang, Ziming and Lin, Wen-Yan and Torr, Philip},
Booktitle = {Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on},
Year = {2014},
Organization = {IEEE},
Pages = {3286--3293},
Owner = {x},
Timestamp = {2015.05.24}
}
@Article{Choi2014,
Title = {Simultaneous Global Localization and Mapping},
Author = {Hyukdoo Choi and Kwang Woong Yang and Euntai Kim},
Journal = {IEEE/ASME Transactions on Mechatronics},
Year = {2014},
Month = {\#aug\#},
Number = {4},
Pages = {1160--1170},
Volume = {19},
File = {Published version:Choi2014.pdf:PDF},
ISSN = {1083-4435},
Owner = {y},
Timestamp = {2014.08.25}
}
@Article{Choi2014a,
Title = {Simultaneous Global Localization and Mapping},
Author = {Hyukdoo Choi and Kwang Woong Yang and Euntai Kim},
Journal = {Mechatronics, IEEE/ASME Transactions on},
Year = {2014},
Month = {Aug},
Number = {4},
Pages = {1160-1170},
Volume = {19},
Doi = {10.1109/TMECH.2013.2274822},
ISSN = {1083-4435},
Keywords = {SLAM (robots);mobile robots;robot vision;SLAM technique;SiGLAM technique;global localization feature-driven method;hypothesis scoring;sensor noise robustness;simultaneous global localization and mapping;Equations;Mathematical model;Noise;Simultaneous localization and mapping;Vectors;Global localization;imperfect map;partially known map;simultaneous localization and mapping (SLAM)},
Owner = {x},
Timestamp = {2015.10.16}
}
@Article{Chow1968,
author = {Chow, C and Liu, C},
title = {Approximating discrete probability distributions with dependence trees},
journal = {IEEE transactions on Information Theory},
year = {1968},
volume = {14},
number = {3},
pages = {462--467},
owner = {cyang},
publisher = {IEEE},
timestamp = {2016.10.01},
}
@Article{Churchill2013,
Title = {Experience-based navigation for long-term localisation},
Author = {Churchill, Winston and Newman, Paul},
Journal = {International Journal of Robotics Research},
Year = {2013},
Number = {14},
Pages = {1645--1661},
Volume = {32},
Abstract = {This paper is about long-term navigation in environments whose appearance changes over time, suddenly or gradually. We describe, implement and validate an approach which allows us to incrementally learn a model whose complexity varies naturally in accordance with variation of scene appearance. It allows us to leverage the state of the art in pose estimation to build over many runs, a world model of sufficient richness to allow simple localisation despite a large variation in conditions. As our robot repeatedly traverses its workspace, it accumulates distinct visual experiences that in concert, implicitly represent the scene variation: each experience captures a visual mode. When operating in a previously visited area, we continually try to localise in these previous experiences while simultaneously running an independent vision-based pose estimation system. Failure to localise in a sufficient number of prior experiences indicates an insufficient model of the workspace and instigates the laying down of the live image sequence as a new distinct experience. In this way, over time we can capture the typical time-varying appearance of an environment and the number of experiences required tends to a constant. Although we focus on vision as a primary sensor throughout, the ideas we present here are equally applicable to other sensor modalities. We demonstrate our approach working on a road vehicle operating over a 3-month period at different times of day, in different weather and lighting conditions. We present extensive results analysing different aspects of the system and approach, in total processing over 136,000 frames captured from 37 km of driving.},
Eprint = {http://ijr.sagepub.com/cgi/reprint/32/14/1645},
File = {Published version:Churchill2013.pdf:PDF},
Owner = {y},
Timestamp = {2014.08.24}
}
@Article{Civera2008,
author = {Civera, Javier and Davison, Andrew J and Montiel, JM Martinez},
title = {Inverse depth parametrization for monocular SLAM},
journal = {IEEE transactions on robotics},
year = {2008},
volume = {24},
number = {5},
pages = {932--945},
publisher = {IEEE},
}
@InProceedings{Civera2011,
author = {Civera, Javier and G{\'a}lvez-L{\'o}pez, Dorian and Riazuelo, Luis and Tard{\'o}s, Juan D and Montiel, JMM},
title = {Towards semantic SLAM using a monocular camera},
booktitle = {Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on},
year = {2011},
pages = {1277--1284},
organization = {IEEE},
file = {Civera2011.pdf:Civera2011.pdf:PDF},
keywords = {qualityAssured, rank3},
owner = {x},
timestamp = {2015.05.24},
}
@InProceedings{Clemente2007,
Title = {Mapping Large Loops with a Single Hand-Held Camera.},
Author = {Clemente, Laura A and Davison, Andrew J and Reid, Ian D and Neira, Jos{\'e} and Tard{\'o}s, Juan D},
Booktitle = {Robotics: Science and Systems},
Year = {2007},
Pages = {11},
Volume = {2},
Owner = {x},
Timestamp = {2015.05.18}
}
@InProceedings{Collet2011,
Title = {Structure discovery in multi-modal data: a region-based approach},
Author = {Collet, Alvaro and Srinivasa, Siddhartha S and Hebert, Martial},
Booktitle = {Robotics and Automation (ICRA), 2011 IEEE International Conference on},
Year = {2011},
Organization = {IEEE},
Pages = {5695--5702},
Owner = {x},
Timestamp = {2015.05.24}
}
@Article{Correa2012,
Title = {Mobile Robots Navigation in Indoor Environments Using Kinect Sensor},
Author = {Correa, D. S. O. and Sciotti, D. F. and Prado, M. G. and Sales, D. O. and Wolf, D. F. and Osorio, F. S.},
Journal = {2012 Second Brazilian Conference on Critical Embedded Systems (CBSEC 2012)},
Year = {2012},
Pages = {36--41},
File = {Published version:Correa2012.pdf:PDF},
Owner = {GaoXiang},
Timestamp = {2014.04.19}
}
@Article{Couprie2013,
Title = {Indoor semantic segmentation using depth information},
Author = {Couprie, Camille and Farabet, Cl{\'e}ment and Najman, Laurent and LeCun, Yann},
Journal = {arXiv preprint arXiv:1301.3572},
Year = {2013},
File = {Couprie2013.pdf:Couprie2013.pdf:PDF},
Owner = {x},
Timestamp = {2015.05.24}
}
@Article{Cummins2008,
author = {Cummins, Mark and Newman, Paul},
journal = {The International Journal of Robotics Research},
title = {FAB-MAP: Probabilistic localization and mapping in the space of appearance},
year = {2008},
number = {6},
pages = {647--665},
volume = {27},
file = {:Cummins2008.pdf:PDF},
groups = {y:5},
keywords = {FAB-MAP, important},
owner = {y},
publisher = {SAGE Publications},
timestamp = {2014.04.16},
url = {http://ijr.sagepub.com/content/27/6/647.full.pdf},
}
@Article{Cummins2010,
Title = {Accelerating FAB-{MAP} With Concentration Inequalities},
Author = {Cummins, Mark and Newman, Paul},
Journal = {IEEE Transactions On Robotics},
Year = {2010},
Number = {6},
Pages = {1042--1050},
Volume = {26},
File = {Published version:Cummins2010.pdf:PDF},
Owner = {GaoXiang},
Timestamp = {2014.01.13}
}
@Article{Cummins2011,
author = {Cummins, Mark and Newman, Paul},
title = {Appearance-only SLAM at large scale with FAB-MAP 2.0},
journal = {International Journal of Robotics Research},
year = {2011},
volume = {30},
number = {9},
pages = {1100--1123},
comment = {著名的FAB-MAP 2.0，做Loop closure的一定要和它去比，不然不完整�?????? 14.11.20 粗略过了�??????遍，FAB-MAP也是基于BoW模型�?????? 感觉LC都是用在很大的场合，本文用的数据高达1000km，全是室外场景，�??????40m之内就算是一个loop。这和Kinect的小而复杂的场景还是非常不一样的�?????? precision-recall曲线的计算： p = true positive / total loops; r = true positive / ground truth 训练方法：在路径上每隔一段取出一张图片，组成训练集�?�对模型训练完毕后，再测试其他的图像，验证是否出现闭环�?�},
file = {Published version:Cummins2011.pdf:PDF},
keywords = {loop closure, FAB-MAP, rank5, qualityAssured},
owner = {GaoXiang},
timestamp = {2014.01.13},
}
@Article{Dardari2015,
Title = {Indoor Tracking: Theory, Methods, and Technologies},
Author = {Dardari, D. and Closas, P. and Djuric, P.M.},
Journal = {Vehicular Technology, IEEE Transactions on},
Year = {2015},
Month = {April},
Number = {4},
Pages = {1263-1278},
Volume = {64},
Doi = {10.1109/TVT.2015.2403868},
ISSN = {0018-9545},
Keywords = {Global Positioning System;mobile communication;signal processing;GPS;global positioning system;high-definition real-time tracking systems;indoor environments;indoor localization;indoor scenarios;indoor tracking;indoor tracking problem;indoor wireless tracking;mobile nodes;outdoor tracking;satellite technologies;signal processing perspective;Accuracy;Estimation;Magnetometers;Mobile nodes;Position measurement;Wireless communication;Bayesian filtering;Indoor tracking;data fusion;indoor tracking;simultaneous localization;simultaneous localization and mapping (SLAM);technologies for tracking},
Owner = {x},
Timestamp = {2015.10.16}
}
@InProceedings{Davison2003,
author = {Davison, Andrew J},
title = {Real-time simultaneous localisation and mapping with a single camera},
booktitle = {Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on},
year = {2003},
pages = {1403--1410},
organization = {IEEE},
}
@Article{Davison2007,
author = {Davison, AJ. and Reid, ID. and Molton, N.D. and Stasse, O.},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
title = {MonoSLAM: Real-Time Single Camera {SLAM}},
year = {2007},
issn = {0162-8828},
number = {6},
pages = {1052--1067},
volume = {29},
comment = {MonoSLAM�????????????经典之作，Davison大大的，英文也写的非常好。},
file = {Published version:Davison2007.pdf:PDF},
groups = {y:5},
keywords = {mono slam, important, classic, rank5, qualityAssured},
owner = {y},
qualityassured = {qualityAssured},
ranking = {rank5},
timestamp = {2014.08.24},
}
@Article{Dellaert2012,
author = {Dellaert, Frank},
title = {Factor graphs and GTSAM: A hands-on introduction},