Application of Least Squares Algorithm on Maps Building for Mobile Robot

In this paper, the laser sensor is used to detect environmental information. The problems of maps building of mobile robot are researched in the indoor structured environment. Least squares algorithm is adopted to build local maps, and a method of dynamic partition are presented based on the distance of adjacent points and angle. For accomplishing the self-localization and route planning of the mobile robot in the indoor environment lacking of the prior map, this paper present the method of the global map building based on the match of the feature points and the characteristic line. The experiments show the methods presented are effective and practicable.

[1]  Zezhong Xu,et al.  Map building and localization using 2D range scanner , 2003, CIRA.

[2]  Ramón Galán,et al.  Building geometric feature based maps for indoor service robots , 2006, Robotics Auton. Syst..

[3]  Feng-Li Lian,et al.  Characterizing Indoor Environment for Robot Navigation Using Velocity Space Approach With Region Analysis and Look-Ahead Verification , 2011, IEEE Transactions on Instrumentation and Measurement.

[4]  Lindsay Kleeman,et al.  Fast Laser Scan Matching using Polar Coordinates , 2007, Int. J. Robotics Res..

[5]  Han Zhonghua.  Application of BP Neural Network Based on PSO in Elevator Group Control System , 2009 .

[6]  Liu Jilin,et al.  Map building and localization using 2D range scanner , 2003, Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694).

[7]  Gamini Dissanayake,et al.  Towards a consistent SLAM algorithm using B-Splines to represent environments , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Songmin Jia,et al.  Consistent Map Building by a mobile robot euipped with stereo sensor and lrf , 2011, 2011 IEEE International Conference on Computer Science and Automation Engineering.

[9]  Juan D. Tardós,et al.  Large-Scale SLAM Building Conditionally Independent Local Maps: Application to Monocular Vision , 2008, IEEE Transactions on Robotics.

[10]  Songmin Jia,et al.  Range scan matching and Particle Filter based mobile robot SLAM , 2010, 2010 IEEE International Conference on Robotics and Biomimetics.