Abstract How to achieve a sparse information matrix exactly is a key issue in sparse extended information filter (SEIF) simultaneous localization and map building (SLAM). A sparsification rule is put forward based on the deep analysis of correlation. The rule can utilize observation information of sparsification time, observe the correlation globally and reserve the features with the strongest correlation. The precision and consistency of the algorithm are improved without an increase of computational burden. Results of Monte-Carlo simulation experiments indicate the validity of the improved algorithm.
[1] Smith R, Self M, Cheeseman P. Estimating Uncertain Spatial Relationships in Robotics // Proc of the IEEE International Conference on Uncertainty in Artificial Intelligence. Amsterdam, Netherlands, 1988: 435-461 [2] Thrun S, Koller D, Ghahmarani Z, et al. SLAM Updates Require Constant Time. Technical Report, CMU-CS-02-112, Pittsburgh, USA: Carnegie Mellon University. School of Computer Science, 2002 [3] Thrun S, Liu Yufeng, Koller D, et al. Simultaneous Localization and Mapping with Sparse Extended Information Filters. International Journal of Robotics Research, 2004, 23(7/8): 693-716 [4] Paskin M A. Thin Junction Tree Filters for Simultaneous Localization and Mapping // Proc of the 18th International Joint Conference on Artificial Intelligence. San Francisco, USA, 2003: 1157-1164 [5] Frese U. An O(log n) Algorithm for Simultaneous Localization and Mapping of Mobile Robots in Indoor Environments. Ph.D Dissertation. Erlanger, Germany: University of Erlangen-Nurnberg. German Aerospace Center, 2004 [6] Frese U. A Proof for the Approximate Sparsity of SLAM Information Matrices // Proc of the IEEE International Conference on Robotics and Automation. Barcelona, Spain, 2005: 329-335 [7] Eustice R, Walter M, Leonard J. Sparse Extended Information Filters: Insights into Sparsification //Proc of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton, Canada, 2005: 3281-3288 [8] Walter M R, Eustice R M, Leonard J J. Exactly Sparse Extended Information Filters for Feature-Based SLAM. International Journal of Robotics Research, 2007, 26(4): 335-359 [9] Li Maohai, Hong Bingrong, Luo Ronghua. Improved Rao-Blackwellized Particle Filters for Mobile Robot Simultaneous Localization and Mapping. Journal of Jilin University: Engineering and Technology Edition, 2007, 37(3): 401-406 (in Chinese) (厉茂海,洪炳熔,罗荣华.用改进的Rao-Blackwellized粒子滤波器实现移动机器人同时定位和地图创建.吉林大学学报:工学版, 2007, 37(2): 401-406) [10] Huang Qingcheng, Hong Bingrong, Li Maohai, et al. Mobile Robot Hierarchical Simultaneous Localization and Mapping Based on Active Loop Closure Constraint. Journal of Computer Research and Development, 2007, 44(4): 636-642 (in Chinese) (黄庆成,洪炳熔,厉茂海,等.基于主动环形闭合约束的移动机器人分层同时定位和地图创建.计算机研究与发展, 2007, 44(4): 636-642) [11] Wang Lu, Cai Zixing. Progress of CML for Mobile Robots in Unknown Environments. Robot, 2004, 26(4): 380-384 (in Chinese) (王 璐,蔡自兴.未知环境中移动机器人并发建图与定位(CML)的研究进展.机器人, 2004, 26(4): 380-384) [12] Bar-Shalom Y, Li R X, Kirubarajan T. Estimation with Applications to Tracking and Navigation. New York, USA: Wiley Interscience, 2001 [13] Dissanayake M W M G, Newman P, Durrant-Whyte H F, et al. A Solution to the Simultaneous Localization and Map Building (SLAM) Problem. IEEE Trans on Robotics and Automation, 2001, 17(3): 229－241 [14] Eustice R M, Singh H, Leonard J J. Exactly Sparse Delayed-State Filters for View-Based SLAM. IEEE Trans on Robotics, 2006, 22(6): 1100-1114
2009, Vol. 22 
