Exploring the effect of meta-structural information on the global consistency of SLAM

Accurate online estimation of the environment structure simultaneously with the robot pose is a key capability for autonomous robotic vehicles. Classical simultaneous localization and mapping (SLAM) algorithms make no assumptions about the configuration of the points in the environment, however, real world scenes have significant structure (ground planes, buildings, walls, ceilings, etc.) that can be exploited. In this paper, we introduce meta-structural information associated with geometric primitives into the estimation problem and analyze their effect on the global structural consistency of the resulting map. Although we only consider the effect of adding planar and orthogonality information for the estimation of 3D points in a Manhattan-like world, this framework can be extended to any type of geometric, kinematic, dynamic or even semantic information. We evaluate our approach on a city-like simulated environment. We highlight the advantages of the proposed solution over SLAM formulation considering no prior knowledge about the configuration of 3D points in the environment.

[1]  Federico Tombari,et al.  Real-time and scalable incremental segmentation on dense SLAM , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[2]  Pavel Zemcík,et al.  Incremental Block Cholesky Factorization for Nonlinear Least Squares in Robotics , 2013, Robotics: Science and Systems.

[3]  Frank Dellaert,et al.  Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing , 2006, Int. J. Robotics Res..

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

[5]  Peter Cheeseman,et al.  A stochastic map for uncertain spatial relationships , 1988 .

[6]  Michael Kaess,et al.  Simultaneous localization and mapping with infinite planes , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Henrik I. Christensen,et al.  Planar surface SLAM with 3D and 2D sensors , 2012, 2012 IEEE International Conference on Robotics and Automation.

[8]  Kurt Konolige,et al.  g 2 o: A general Framework for (Hyper) Graph Optimization , 2011 .

[9]  Frank Dellaert,et al.  iSAM: Incremental Smoothing and Mapping , 2008, IEEE Transactions on Robotics.

[10]  Diego Rodríguez-Losada,et al.  Feature based graph-SLAM in structured environments , 2014, Auton. Robots.

[11]  Davide Scaramuzza,et al.  Benefit of large field-of-view cameras for visual odometry , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Jeffrey K. Uhlmann,et al.  A counter example to the theory of simultaneous localization and map building , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[13]  Richard Szeliski,et al.  Geometrically Constrained Structure from Motion: Points on Planes , 1998, SMILE.

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

[15]  Chen Feng,et al.  Point-plane SLAM for hand-held 3D sensors , 2013, 2013 IEEE International Conference on Robotics and Automation.

[16]  Audra E. Kosh,et al.  Linear Algebra and its Applications , 1992 .

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

[18]  Wolfram Burgard,et al.  A statistical measure for map consistency in SLAM , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[20]  Pavel Zemcík,et al.  Efficient implementation for block matrix operations for nonlinear least squares problems in robotic applications , 2013, 2013 IEEE International Conference on Robotics and Automation.

[21]  Henrik I. Christensen,et al.  Constrained structure and motion estimation from optical flow , 2002, Object recognition supported by user interaction for service robots.

[22]  Wolfram Burgard,et al.  A Tutorial on Graph-Based SLAM , 2010, IEEE Intelligent Transportation Systems Magazine.

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

[24]  Ingemar J. Cox,et al.  Dynamic Map Building for an Autonomous Mobile Robot , 1992 .

[25]  Frank Dellaert,et al.  iSAM2: Incremental smoothing and mapping using the Bayes tree , 2012, Int. J. Robotics Res..

[26]  Se-Young Oh,et al.  Indoor mapping using planes extracted from noisy RGB-D sensors , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[27]  John J. Leonard,et al.  A Mixture of Manhattan Frames: Beyond the Manhattan World , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Olivier Faugeras,et al.  Maintaining representations of the environment of a mobile robot , 1988, IEEE Trans. Robotics Autom..

[29]  Wolfram Burgard,et al.  Improving Simultaneous Mapping and Localization in 3D Using Global Constraints , 2005, AAAI.