2D SLAM quality evaluation methods

SLAM (Simultaneous Localization and mapping) is one of the most challenging problems for mobile platforms and there is a huge amount of modern SLAM algorithms. The choice of the algorithm that might be used in every particular problem requires prior knowledge about advantages and disadvantages of each algorithm. This paper presents the approach for comparison of SLAM algorithms that allows to find the most accurate one. The accent of research is made on 2D SLAM algorithms and the focus of analysis is 2D map that is built after algorithm performance. Three metrics for evaluation of maps are presented in this paper.

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

[2]  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.

[3]  Bhaskara Marthi,et al.  An object-based semantic world model for long-term change detection and semantic querying , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Robin Jaulmes,et al.  Towards a quantitative evaluation of simultaneous localization and mapping methods , 2009 .

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

[6]  Kirill Krinkin,et al.  Evaluation of the modern visual SLAM methods , 2015, 2015 Artificial Intelligence and Natural Language and Information Extraction, Social Media and Web Search FRUCT Conference (AINL-ISMW FRUCT).

[7]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[8]  John J. Leonard,et al.  The MIT Stata Center dataset , 2013, Int. J. Robotics Res..

[9]  Kirill Krinkin,et al.  TinySLAM improvements for indoor navigation , 2016, 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[10]  J. O’Kane A Gentle Introduction to ROS , 2016 .

[11]  Haoran Li,et al.  Comparison of methods to efficient graph SLAM under general optimization framework , 2017, 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[12]  David Portugal,et al.  An evaluation of 2D SLAM techniques available in Robot Operating System , 2013, 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

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

[14]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[15]  Tobias Pietzsch,et al.  A Framework For Evaluating Visual SLAM , 2009, BMVC.

[16]  Kirill Krinkin,et al.  VinySLAM: An indoor SLAM method for low-cost platforms based on the Transferable Belief Model , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[17]  Arthur Huletski,et al.  A SLAM research framework for ROS , 2016, CEE-SECR '16.

[18]  Keiichi Abe,et al.  Topological structural analysis of digitized binary images by border following , 1985, Comput. Vis. Graph. Image Process..

[19]  Dirk Eddelbuettel A Gentle Introduction to Rcpp , 2013 .

[20]  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.