A scan matching method using Euclidean invariant signature for global localization and map building

This work presents a new scan matching method for mobile robot localization and mapping. The proposed method is based on the geometric hashing scheme, which utilizes Euclidean invariant features in order to match an input scan with reference scans without an initial alignment The method is applicable to global localization in an environment having curved objects. Experimental results show that a map of a large cyclic environment was built with high accuracy using the proposed method.

[1]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[2]  Hugh F. Durrant-Whyte,et al.  Simultaneous map building and localization for an autonomous mobile robot , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

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

[4]  Ewald von Puttkamer,et al.  Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

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

[6]  Evangelos E. Milios,et al.  Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans , 1997, J. Intell. Robotic Syst..

[7]  Bernhard Nebel,et al.  Fast, accurate, and robust self-localization in polygonal environments , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[8]  J. M. M. Montiel,et al.  The SPmap: a probabilistic framework for simultaneous localization and map building , 1999, IEEE Trans. Robotics Autom..

[9]  Joachim Weber,et al.  APR - Global Scan Matching Using Anchor Point Relationships , 2000 .

[10]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[11]  Masahiro Tomono,et al.  Object-based localization and mapping using loop constraints and geometric prior knowledge , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[12]  Wolfram Burgard,et al.  A system for volumetric robotic mapping of abandoned mines , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[13]  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).

[14]  Sebastian Thrun,et al.  Robotic mapping: a survey , 2003 .