Appearance‐based landmark selection for visual localization

[1]  Winston Churchill,et al.  Experience-based navigation for long-term localisation , 2013, Int. J. Robotics Res..

[2]  Henning Lategahn,et al.  DIRD is an illumination robust descriptor , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[3]  Paul Newman,et al.  Work smart, not hard: Recalling relevant experiences for vast-scale but time-constrained localisation , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Ryan M. Eustice,et al.  University of Michigan North Campus long-term vision and lidar dataset , 2016, Int. J. Robotics Res..

[5]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[6]  Roland Siegwart,et al.  Appearance-based landmark selection for efficient long-term visual localization , 2016, IROS 2016.

[7]  Roland Siegwart,et al.  Map Management for Efficient Long-Term Visual Localization in Outdoor Environments , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[8]  Guang-Zhong Yang,et al.  Feature Co-occurrence Maps: Appearance-based localisation throughout the day , 2013, 2013 IEEE International Conference on Robotics and Automation.

[9]  Michael Warren,et al.  Bridging the appearance gap: Multi-experience localization for long-term visual teach and repeat , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[10]  Akiko Aizawa,et al.  An information-theoretic perspective of tf-idf measures , 2003, Inf. Process. Manag..

[11]  Gordon Wyeth,et al.  Experience mapping: Producing spatially continuous environment representations using RatSLAM , 2005 .

[12]  Paul Newman,et al.  Shady dealings: Robust, long-term visual localisation using illumination invariance , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[13]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Tim D. Barfoot,et al.  It's not easy seeing green: Lighting-resistant stereo Visual Teach & Repeat using color-constant images , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Grzegorz Cielniak,et al.  An Adaptive Spherical View Representation for Navigation in Changing Environments , 2009, ECMR.

[16]  Gordon Wyeth,et al.  Persistent Navigation and Mapping using a Biologically Inspired SLAM System , 2010, Int. J. Robotics Res..

[17]  Guang-Zhong Yang,et al.  Generative Methods for Long-Term Place Recognition in Dynamic Scenes , 2013, International Journal of Computer Vision.

[18]  Timothy D. Barfoot,et al.  Robust Monocular Visual Teach and Repeat Aided by Local Ground Planarity and Color-constant Imagery , 2017, J. Field Robotics.

[19]  Michael Bosse,et al.  Summary Maps for Lifelong Visual Localization , 2016, J. Field Robotics.

[20]  Daniel P. Huttenlocher,et al.  Location Recognition Using Prioritized Feature Matching , 2010, ECCV.

[21]  Christian Schlegel,et al.  Towards a robust visual SLAM approach: Addressing the challenge of life-long operation , 2009, 2009 International Conference on Advanced Robotics.

[22]  Michael Bosse,et al.  Keep it brief: Scalable creation of compressed localization maps , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[23]  Tim D. Barfoot,et al.  Visual triage: A bag-of-words experience selector for long-term visual route following , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[24]  Peter Mühlfellner,et al.  Designing a Relational Database for Long-Term Visual Mapping , 2015 .

[25]  Vincent Lepetit,et al.  BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Simon Lacroix,et al.  Location graphs for visual place recognition , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[27]  Jana Kosecka,et al.  Probabilistic location recognition using reduced feature set , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[28]  Kurt Konolige,et al.  Towards lifelong visual maps , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Richard Szeliski,et al.  City-Scale Location Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Paul Newman,et al.  Scene Signatures: Localised and Point-less Features for Localisation , 2014, Robotics: Science and Systems.

[31]  Torsten Sattler,et al.  Fast image-based localization using direct 2D-to-3D matching , 2011, 2011 International Conference on Computer Vision.

[32]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[33]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Paul Newman,et al.  Appearance-only SLAM at large scale with FAB-MAP 2.0 , 2011, Int. J. Robotics Res..

[35]  Gordon Wyeth,et al.  Outdoor Simultaneous Localisation and Mapping Using RatSLAM , 2005, FSR.

[36]  Wolfram Burgard,et al.  A comparison of SLAM algorithms based on a graph of relations , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[37]  Gordon Wyeth,et al.  RatSLAM: a hippocampal model for simultaneous localization and mapping , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.