A global localization approach based on relative position relationships of line segments

Scan matching is a method for mobile robot global localization in indoor structured environment. The disadvantages of current methods are low computational efficiency and large probability of error matching in the large-scale environment. In this paper, we proposed a new scanmatching method for global localization with the relative position relationship of complete line segments (CLS) and visible complete line segments (VCLS). The proposed method compared the relative relationship of CLS in local map and VCLS in ordinal global map to avoid excessive coordinate transformation and limits of line segments in matching between the local map and the global map. In the simulations, new method can obviously reduce computation cost in large-scale environment.

[1]  Xiong Chen,et al.  Prediction-based geometric feature extraction for 2D laser scanner , 2011, Robotics Auton. Syst..

[2]  Xiaolong Xu,et al.  Exponentially Weighted Particle Filter for Simultaneous Localization and Mapping Based on Magnetic Field Measurements , 2017, IEEE Transactions on Instrumentation and Measurement.

[3]  Tieniu Tan,et al.  Mobile robot self-localization based on global visual appearance features , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[4]  Andreas Zell,et al.  Vector-AMCL: Vector Based Adaptive Monte Carlo Localization for Indoor Maps , 2016, IAS.

[5]  Wan Kyun Chung,et al.  Global localization for a small mobile robot using magnetic patterns , 2010, 2010 IEEE International Conference on Robotics and Automation.

[6]  Zhiyu Xiang,et al.  Initial localization for an indoor mobile robot using a laser range finder , 2002, SPIE Optics East.

[7]  Hui Shen,et al.  A Global Line Matching algorithm for 2D laser scan matching in regular environment , 2010, 2010 IEEE Safety Security and Rescue Robotics.

[8]  Masahiro Tomono,et al.  Robust robot localization and map building using a global scan matching method , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[9]  Hugh Durrant-Whyte,et al.  Simultaneous localization and mapping (SLAM): part II , 2006 .

[10]  Wolfram Burgard,et al.  On the position accuracy of mobile robot localization based on particle filters combined with scan matching , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[12]  Prabir K. Pal,et al.  A novel method for computation of importance weights in Monte Carlo localization on line segment-based maps , 2015, Robotics Auton. Syst..

[13]  Liu Jilin,et al.  Scan matching based on CLS relationships , 2003, IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003.

[14]  Soon-Yyong Park,et al.  Global localization for mobile robots using reference scan matching , 2014 .

[15]  Zhao Yilu Scan Matching Based SLAM in Outdoor Environment , 2010 .

[16]  Wu Jun Incremental mapping based on dot-line congruence for robot , 2007 .

[17]  Chen Friedman,et al.  Perimeter-Based Polar Scan Matching (PB-PSM) for 2D Laser Odometry , 2015, J. Intell. Robotic Syst..