On behalf of: Multimedia Archives

Simultaneous Localization and Mapping (SLAM) has focused on noisy but unique data associations resulting in linear Gaussian uncertainty models. However, a unique decision is often not possible using only local information, giving rise to ambiguities that have to be resolved globally during optimization. To solve this problem, the pose graph data structure is extended here by multimodal constraints modeled by mixtures of Gaussians (MoG). Furthermore, optimization methods for this novel formulation are introduced, namely (a) robust iteratively reweighted least squares, and (b) Prefilter Stochastic Gradient Descent (SGD) where a preprocessing step determines globally consistent modes before applying SGD. In addition, a variant of the Prefilter method (b) is introduced in form of (c) Prefilter Levenberg-Marquardt. The methods are compared with traditional state-of-the-art optimization methods including (d) Stochastic Gradient Descent and (e) Levenberg-Marquardt as well as (f) Particle filter SLAM and with (g) an optimal exhaustive algorithm. Experiments show that ambiguities significantly impact state-of-the-art methods, and that the novel Prefilter methods (b) and (c) perform best. This is further substantiated with experiments using real-world data. To this end, a method to generate MoG constraints from a plane-based registration algorithm is introduced and used for 3D SLAM under ambiguities.

[1]  Matteo Golfarelli,et al.  Correction of dead-reckoning errors in map building for mobile robots , 2001, IEEE Trans. Robotics Autom..

[2]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  James J. Little,et al.  /spl sigma/SLAM: stereo vision SLAM using the Rao-Blackwellised particle filter and a novel mixture proposal distribution , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[4]  Andreas Birk,et al.  Plane-based registration of sonar data for underwater 3D mapping , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  John J. Craig Zhu,et al.  Introduction to robotics mechanics and control , 1991 .

[6]  Wolfram Burgard,et al.  Analyzing gaussian proposal distributions for mapping with rao-blackwellized particle filters , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[8]  Wolfram Burgard,et al.  Spatially-Adaptive Learning Rates for Online Incremental SLAM , 2008 .

[9]  Wolfram Burgard,et al.  Online constraint network optimization for efficient maximum likelihood map learning , 2008, 2008 IEEE International Conference on Robotics and Automation.

[10]  H. Saunders,et al.  Book Reviews : Fracture and Fatigue Control in Structures - Application of Fracture Mechanics: S.T. Rolfe and J.M. Barsom Prentice-Hall, Inc., Englewood Cliffs, NJ, 1977 , 1979 .

[11]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

[12]  Udo Frese,et al.  A Discussion of Simultaneous Localization and Mapping , 2006, Auton. Robots.

[13]  Garrison W. Cottrell,et al.  Gamma‐SLAM: Visual SLAM in unstructured environments using variance grid maps , 2009, J. Field Robotics.

[14]  Andreas Birk,et al.  Online three-dimensional SLAM by registration of large planar surface segments and closed-form pose-graph relaxation , 2010 .

[15]  Tom Duckett,et al.  A multilevel relaxation algorithm for simultaneous localization and mapping , 2005, IEEE Transactions on Robotics.

[16]  Wolfram Burgard,et al.  Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM After Actively Closing Loops , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[17]  R. K. Shyamasundar,et al.  Introduction to algorithms , 1996 .

[18]  Masahiro Tomono,et al.  Monocular SLAM Using a Rao-Blackwellised Particle Filter with Exhaustive Pose Space Search , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[19]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[20]  Stefan B. Williams,et al.  An efficient approach to bathymetric SLAM , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  David Wettergreen,et al.  Towards particle filter SLAM with three dimensional evidence grids in a flooded subterranean environment , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[22]  David Wettergreen,et al.  Real‐Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels , 2007, J. Field Robotics.

[23]  Wolfram Burgard,et al.  A Tree Parameterization for Efficiently Computing Maximum Likelihood Maps using Gradient Descent , 2007, Robotics: Science and Systems.

[24]  Horst-Michael Groß,et al.  A graph matching technique for an appearance-based, visual SLAM-approach using Rao-Blackwellized Particle Filters , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.

[26]  P. J. Huber Robust Regression: Asymptotics, Conjectures and Monte Carlo , 1973 .

[27]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[28]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[29]  Wolfram Burgard,et al.  Efficient Sparse Pose Adjustment for 2D mapping , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[30]  Christoph Hertzberg,et al.  A Framework for Sparse, Non-Linear Least Squares Problems on Manifolds-Ein Rahmen für dünnbesetzte, nichtlineare quadratische Ausgleichsrechnung auf Mannigfaltigkeiten , 2008 .

[31]  Niko Sünderhauf,et al.  Towards a robust back-end for pose graph SLAM , 2012, 2012 IEEE International Conference on Robotics and Automation.

[32]  William Feller,et al.  The fundamental limit theorems in probability , 1945 .

[33]  Edwin Olson,et al.  Robust and efficient robotic mapping , 2008 .

[34]  Andreas Birk,et al.  Fast Registration Based on Noisy Planes With Unknown Correspondences for 3-D Mapping , 2010, IEEE Transactions on Robotics.

[35]  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.

[36]  Wolfram Burgard,et al.  G2o: A general framework for graph optimization , 2011, 2011 IEEE International Conference on Robotics and Automation.

[37]  Cyrill Stachniss,et al.  Hierarchical optimization on manifolds for online 2D and 3D mapping , 2010, 2010 IEEE International Conference on Robotics and Automation.

[38]  Frank Dellaert,et al.  Square Root SAM , 2005, Robotics: Science and Systems.

[39]  Horst-Michael Groß,et al.  A sensor-independent approach to RBPF SLAM - Map Match SLAM applied to visual mapping , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[40]  Armin B. Cremers,et al.  Optimization techniques for laser-based 3D particle filter SLAM , 2010, 2010 IEEE International Conference on Robotics and Automation.

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

[42]  Y. Charlie Hu,et al.  Simultaneous localization and mapping with environmental structure prediction , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[43]  Andreas Birk,et al.  A quantitative assessment of structural errors in grid maps , 2010, Auton. Robots.

[44]  Edwin Olson,et al.  Fast iterative alignment of pose graphs with poor initial estimates , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[45]  Cyrill Stachniss,et al.  On measuring the accuracy of SLAM algorithms , 2009, Auton. Robots.

[46]  Andreas Birk,et al.  Evaluation of the robustness of planar-patches based 3D-registration using marker-based ground-truth in an outdoor urban scenario , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[47]  Wolfram Burgard,et al.  Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[48]  Y. Charlie Hu,et al.  P-SLAM: Simultaneous Localization and Mapping With Environmental-Structure Prediction , 2007, IEEE Transactions on Robotics.

[49]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[50]  Randall Smith,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[51]  Giulio Sandini,et al.  Tactile Sensing—From Humans to Humanoids , 2010, IEEE Transactions on Robotics.

[52]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[53]  Wolfram Burgard,et al.  Efficient estimation of accurate maximum likelihood maps in 3D , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[54]  Kevin P. Murphy,et al.  Bayesian Map Learning in Dynamic Environments , 1999, NIPS.

[55]  Andreas Birk,et al.  Online 3D SLAM by Registration of Large Planar Surface Segments and Closed Form Pose-Graph Relaxation , 2010 .

[56]  Joachim Hertzberg,et al.  Globally consistent 3D mapping with scan matching , 2008, Robotics Auton. Syst..

[57]  Edwin Olson,et al.  Recognizing places using spectrally clustered local matches , 2009, Robotics Auton. Syst..

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

[59]  Sebastian Thrun,et al.  Simultaneous localization and mapping with unknown data association using FastSLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[60]  Wolfram Burgard,et al.  An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).