Research of Mobile Robot SLAM Based on EKF and its Improved Algorithms

Extended Kalman Filter (EKF) based solution is one of the most popular techniques for solving mobile robot Simultaneous Localization and Mapping (SLAM) problem. In this paper, the basic algorithm of EKF based SLAM and its improved algorithms are introduced. The improved algorithms are mainly on two aspects: data association and computational complexity. First, the classical data association algorithm, Individual Compatibility Nearest Neighbor (ICNN), is presented. And two improved methods including batch validation gating and multi-hypothesis are also introduced. Then, partitioned updates and submapping methods are introduced as the main ones of reducing computational complexity. Some representative improved algorithms are presented. These algorithms enable EKF to solve the mobile robot SLAM problem in cluttered and large scale environments.

[1]  John J. Leonard,et al.  Decoupled stochastic mapping [for mobile robot & AUV navigation] , 2001 .

[2]  Eduardo Mario Nebot,et al.  Optimization of the simultaneous localization and map-building algorithm for real-time implementation , 2001, IEEE Trans. Robotics Autom..

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

[4]  Zhao Chunxia,et al.  Improved joint compatibility data association approach for simultaneous localization and map building(SLAM) , 2008 .

[5]  Eduardo Mario Nebot,et al.  Consistency of the EKF-SLAM Algorithm , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Yu Hong-shan.  A review of simultaneous localization and map building algorithms for mobile robots in unknown environment , 2008 .

[7]  John J. Leonard,et al.  Robust Mapping and Localization in Indoor Environments Using Sonar Data , 2002, Int. J. Robotics Res..

[8]  Stergios I. Roumeliotis,et al.  Power-SLAM: A linear-complexity, consistent algorithm for SLAM , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Gamini Dissanayake,et al.  Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM , 2007, IEEE Transactions on Robotics.

[10]  J. Leonard,et al.  Decoupled Stochastic Mapping , 2001 .

[11]  Juan D. Tardós,et al.  Data association in stochastic mapping using the joint compatibility test , 2001, IEEE Trans. Robotics Autom..

[12]  Sebastian Thrun,et al.  Simultaneous localization and mapping with unknown data association using FastSLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[13]  Patric Jensfelt,et al.  EKF SLAM updates in O(n) with Divide and Conquer SLAM , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[14]  José A. Castellanos,et al.  Linear time vehicle relocation in SLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[15]  Hugh F. Durrant-Whyte,et al.  Data association for mobile robot navigation: a graph theoretic approach , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[16]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

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

[18]  Juan D. Tardós,et al.  Hierarchical SLAM: real-time accurate mapping of large environments , 2005, IEEE Transactions on Robotics.

[19]  Ramón Galán,et al.  Consistency improvement for SLAM - EKF for indoor environments , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[20]  Edwin Olson,et al.  Single-Cluster Spectral Graph Partitioning for Robotics Applications , 2005, Robotics: Science and Systems.

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

[22]  Ian D. Reid,et al.  Towards constant time SLAM using postponement , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).