Simulation research for active Simultaneous Localization and Mapping based on Extended Kalman Filter

Simultaneous localization and mapping (SLAM) problem has caused wide concern in the robotics research. The active SLAM algorithm based on extended Kalman filter (EKF) is considered as an important method. To validate and analyze the active SLAM algorithm performance, a simulation system is designed and developed, which makes use of the MATLAB platform as computation engine and properly displays the process of SLAM. Active SLAM algorithms can be simulated and analyzed through the simulation experiments on this platform in both visual and statistical fashions. Algorithm simulation examples show that the simulation system makes the research for the active SLAM efficient.

[1]  Wolfram Burgard,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998, Auton. Robots.

[2]  Yalou Huang,et al.  Motion Planning for SLAM Based on Frontier Exploration , 2007, 2007 International Conference on Mechatronics and Automation.

[3]  John J. Leonard,et al.  Adaptive Mobile Robot Navigation and Mapping , 1999, Int. J. Robotics Res..

[4]  Chao Li,et al.  A Solution to Active Simultaneous Localization and Mapping Problem Based on Optimal Control , 2007, 2007 International Conference on Mechatronics and Automation.

[5]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[6]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.

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

[8]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[9]  Peter Cheeseman,et al.  A stochastic map for uncertain spatial relationships , 1988 .

[10]  Randall Smith,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[11]  Jean-Paul Laumond,et al.  Position referencing and consistent world modeling for mobile robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[12]  Brian Yamauchi,et al.  A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[13]  Stefan B. Williams.  Efficient Solutions to Autonomous Mapping and Navigation Problems , 2009 .

[14]  Olivier D. Faugeras,et al.  Maintaining representations of the environment of a mobile robot , 1988, IEEE Trans. Robotics Autom..

[15]  Hugh F. Durrant-Whyte,et al.  Simultaneous Localization and Mapping with Sparse Extended Information Filters , 2004, Int. J. Robotics Res..