邹宇华, 陈伟海, 王建华, 吴星明. 基于信息增益一致性的多机器人地图融合算法[J]. 机器人, 2014, 36(5): 619-626.DOI: 10.13973/j.cnki.robot.2014.0619.
ZOU Yuhua, CHEN Weihai, WANG Jianhua, WU Xingming. Multi-Robot Map Merging Based on the Consistency of Information Gain. ROBOT, 2014, 36(5): 619-626. DOI: 10.13973/j.cnki.robot.2014.0619.
In map merging in multi-robot cooperative SLAM (simultaneous localization and mapping), global map construction maybe fails due to information deficiency caused by limited communication range or communication topology changes of the multi-robot network. To solve the problem, a new dynamic map merging algorithm is proposed based on the consensus of information gain. The proposed algorithm is fully distributed and independent of any specific communication topology. The information gain between the new observed data and the history data of the local map estimated by each robot is calculated and utilized to enable each robot to achieve consentaneous global map simultaneously. The proposed algorithm can asymptotically converge to the true global map under limited communication conditions. Furthermore, the estimated global map of each robot is unbiased in each iteration step. RGB-D (RGB-depth) data collected from real world are used to confirm the efficiency of the proposed algorithm.
[1] Howard A. Multi-robot simultaneous localization and mapping using particle filters[J]. International Journal of Robotics Research, 2006, 25(12): 1243-1256. [2] Vincent R, Fox D, Ko J, et al. Distributed multirobot exploration, mapping, and task allocation[J]. Annals of Mathematics and Artificial Intelligence, 2008, 52(2-4): 229-255. [3] Paz L M, Tardós J D, Neira J. Divide and conquer: EKF SLAM in O(n)[J]. IEEE Transactions on Robotics, 2008, 24(5): 1107-1120. [4] Aragues R, Cortes J, Sagues C. Distributed consensus on robot networks for dynamically merging feature-based maps[J]. IEEE Transactions on Robotics, 2012, 28(4): 840-854. [5] 任孝平,蔡自兴,陈爱斌.多移动机器人通信系统研究进展[J].控制与决策,2010,25(3):327-332,338. Ren X P, Cai Z X, Chen A B. Current research in multi-mobile robots communication system[J]. Control and Decision, 2010,25(3): 327-332,338.[6] Nebot E M, Bozorg M, Durrant-Whyte H F. Decentralized architecture for asynchronous sensors[J]. Autonomous Robots,1999, 6(2): 147-164. [7] Grime S, Durrant-Whyte H F. Data fusion in decentralized sensor networks[J]. Control Engineering Practice, 1994, 2(5): 849-863. [8] Julier S J, Uhlmann J K. General decentralized data fusion with covariance intersection[M]//Handbook of Multisensor Data Fusion. Boca Raton, USA: CRC Press, 2001.[9] Olfati-Saber R. Distributed Kalman filtering for sensor networks[C]//IEEE Conference on Decision and Control. Piscataway, USA: IEEE, 2007: 5492-5498.[10] Alriksson P, Rantzer A. Distributed Kalman filtering using weighted averaging[C/CD]//17th International Symposium on Mathematical Theory of Networks and Systems. 2006.[11] 吴晓琳,宋萌,苑晶,等.通讯范围受限条件下的多机器人主动SLAM[J].系统工程与电子技术,2012,34(10):2121-2128. Wu X L, Song M, Yuan J, et al. Multi-robot active SLAM under limited communication range[J]. System Engineering and Electronics, 2012, 34(10): 2121-2128.[12] Casbeer D W, Beard R. Distributed information filtering using consensus filters[C]//American Control Conference. Piscataway, USA: IEEE, 2009: 1882-1887.[13] Carli R, Chiuso A, Schenato L, et al. Distributed Kalman filtering based on consensus strategies[J]. IEEE Journal on Selected Areas in Communications, 2008, 26(4): 622-633. [14] Leung K Y K, Barfoot T D, Liu H H T. Decentralized cooperative simultaneous localization and mapping for dynamic and sparse robot networks[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE,2010: 3554-3561.[15] Cunningham A, Wurm K M, Burgard W, et al. Fully distributed scalable smoothing and mapping with robust multi-robot data association[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA: IEEE, 2012: 1093-1100.[16] Aragues R, Montijano E, Sagues C. Consistent data association in multi-robot systems with limited communications [C]//Robotics: Science and Systems VI. Cambridge, USA: MIT Press, 2010: 97-104.[17] Xiao L, Boyd S, Lall S. A space-time diffusion scheme for peer-to-peer least-squares estimation[C]//5th International Conference on Information Processing in Sensor Networks. New York, USA: ACM, 2006: 168-176.[18] Calafiore G C, Abrate F. Distributed linear estimation over sensor networks[J]. International Journal of Control, 2009, 82(5):868-882. [19] Thrun S, Liu Y, Koller D, et al. Simultaneous localization and mapping with sparse extended information filters[J]. International Journal of Robotics Research, 2004, 23(7/8): 693-716.