Hierarchical SLAM using spectral submap matching with opportunities for long-term operation

We present a hierarchical SLAM approach which uses spectral registration of local submaps to close loops and to perform global localization after a restart. Using the Fourier-Mellin Transform (FMT), we robustly register occupancy grid representations of local submaps and present methods which improve matching performance. We further show how good match candidates can be reliably detected even from scaled-down versions of the submaps, which significantly reduces the computation time. The spectral registration approach proves useful even in the presence of significant environmental changes due to the fact that it calculates a dense match, incorporating all observed information rather than a sparse set of features.

[1]  Pat Langley,et al.  Place recognition in dynamic environments , 1997 .

[2]  Andreas Birk,et al.  Fast and robust photomapping with an Unmanned Aerial Vehicle (UAV) , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Andreas Birk,et al.  Merging Occupancy Grid Maps From Multiple Robots , 2006, Proceedings of the IEEE.

[4]  Tim Kazik,et al.  Visual odometry based on the Fourier-Mellin transform for a rover using a monocular ground-facing camera , 2011, 2011 IEEE International Conference on Mechatronics.

[5]  Javier González,et al.  Subjective local maps for hybrid metric-topological SLAM , 2009, Robotics Auton. Syst..

[6]  Michael Bosse,et al.  An Atlas framework for scalable mapping , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[7]  Andreas Birk,et al.  Online generation of an underwater photo map with improved Fourier Mellin based registration , 2009, OCEANS 2009-EUROPE.

[8]  Robert B. Fisher,et al.  A Comparison of Algorithms for Subpixel Peak Detection , 1996 .

[9]  Roland Chapuis,et al.  Radar Scan Matching SLAM Using the Fourier-Mellin Transform , 2009, FSR.

[10]  Javier González,et al.  Toward a Unified Bayesian Approach to Hybrid Metric--Topological SLAM , 2008, IEEE Transactions on Robotics.

[11]  Wolfram Burgard,et al.  Geometrical FLIRT phrases for large scale place recognition in 2D range data , 2013, 2013 IEEE International Conference on Robotics and Automation.

[12]  Luca Iocchi,et al.  Scan Matching in the Hough Domain , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[13]  Andreas Birk,et al.  Maximum likelihood mapping with spectral image registration , 2010, 2010 IEEE International Conference on Robotics and Automation.

[14]  Dirk R. Padfield,et al.  Masked Object Registration in the Fourier Domain , 2012, IEEE Transactions on Image Processing.

[15]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[16]  Pat Langley,et al.  Place recognition in dynamic environments , 1997, J. Field Robotics.

[17]  Kai Oliver Arras,et al.  FLIRT - Interest regions for 2D range data , 2010, 2010 IEEE International Conference on Robotics and Automation.

[18]  F. Dellaert.  Factor Graphs and GTSAM: A Hands-on Introduction , 2012 .

[19]  Andreas Birk.  Using recursive spectral registrations to determine brokenness as measure of structural map errors , 2010, 2010 IEEE International Conference on Robotics and Automation.

[20]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[21]  B. N. Chatterji,et al.  An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..

[22]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.