A Triangle Feature Based Map-to-map Matching and Loop Closure for 2D Graph SLAM

The loop closure problem in 2D LiDAR simultaneous localization and mapping (SLAM) is interesting yet to be solved efficiently, as it suffers from a lack of information in 2D laser rangefinder readings. For this reason, we compare two grid submaps, where features and constraints are enriched, to find loops. We propose a geometric environment descriptor called a Triangle Feature (TF). It exploits the Euclidean distance constraint any three feature points in a submap can form. A 2D graph SLAM system using TF to close loops is also developed. The global maps we build outperform some popular open-source SLAM solutions, and the system can run up to 2.4 times of real-time in our experiments.

[1]  Stefan Kohlbrecher,et al.  A flexible and scalable SLAM system with full 3D motion estimation , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[2]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

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

[4]  Sven Hellbach,et al.  Large scale place recognition in 2D LIDAR scans using Geometrical Landmark Relations , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Edwin Olson,et al.  Real-time correlative scan matching , 2009, 2009 IEEE International Conference on Robotics and Automation.

[6]  Wolfgang Hess,et al.  Real-time loop closure in 2D LIDAR SLAM , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

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

[8]  Andrea Censi,et al.  An ICP variant using a point-to-line metric , 2008, 2008 IEEE International Conference on Robotics and Automation.

[9]  Yonina C. Eldar,et al.  A probabilistic Hough transform , 1991, Pattern Recognit..

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

[11]  S. Umeyama,et al.  Least-Squares Estimation of Transformation Parameters Between Two Point Patterns , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

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

[13]  Javier González,et al.  A robust, multi-hypothesis approach to matching occupancy grid maps , 2013, Robotica.

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

[15]  François Michaud,et al.  Online global loop closure detection for large-scale multi-session graph-based SLAM , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Stefano Carpin,et al.  Fast and accurate map merging for multi-robot systems , 2008, Auton. Robots.

[17]  J. Munkres.  ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

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

[19]  John J. Leonard,et al.  Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age , 2016, IEEE Transactions on Robotics.

[20]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[21]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[22]  유원석.  Scan Similarity-based Pose Graph Construction Method for Graph SLAM , 2018 .

[23]  Paul J. Besl,et al.  Method for registration of 3-D shapes , 1992, Other Conferences.

[24]  Edwin Olson,et al.  M3RSM: Many-to-many multi-resolution scan matching , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[25]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.