[1]陈孟元.鼠类脑细胞导航机理的移动机器人仿生SLAM综述[J].智能系统学报,2018,13(01):107-117.[doi:10.11992/tis.201707003]
 CHEN Mengyuan.Overview of mobile robot bionic slam based on navigation mechanism of mouse brain cells[J].CAAI Transactions on Intelligent Systems,2018,13(01):107-117.[doi:10.11992/tis.201707003]
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鼠类脑细胞导航机理的移动机器人仿生SLAM综述(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年01期
页码:
107-117
栏目:
出版日期:
2018-01-24

文章信息/Info

Title:
Overview of mobile robot bionic slam based on navigation mechanism of mouse brain cells
作者:
陈孟元12
1. 安徽工程大学 安徽省电气传动与控制重点实验室, 安徽 芜湖 241000;
2. 中国科学技术大学 精密机械与精密仪器系, 安徽 合肥 230027
Author(s):
CHEN Mengyuan12
1. Anhui Key Laboratory of Electric Drive and Control, Anhui Polytechnic University, Wuhu 241000, China;
2. Dept Precis Machinery and Precis Instrumentat, University of Science and Technology of China, Hefei 230027, China
关键词:
移动机器人同步定位和地图构建鼠类脑细胞闭环检测关键帧匹配
Keywords:
mobile robotsimultaneous localization and mappingrodentsbrain cellsclosed loop detectionkeyframe matching
分类号:
TP242.6;TP751
DOI:
10.11992/tis.201707003
摘要:
针对同步定位与地图构建(SLAM)问题中传统概率算法存在计算量大、复杂度高、易陷于局部最优解等问题,本文提出一种未来深入研究的方法建议,将鼠类脑细胞中边界细胞(border cells)、局部场景细胞(view cells)、网格细胞(grid cells)、速度细胞(speed cells)、位姿细胞(pose cells)等具有定位导航功能的细胞应用于SLAM研究中,构建一种基于多细胞导航机制的BVGSP-SLAM模型。结合具有实时关键帧匹配的闭环检测算法以避免光线变化对SLAM的影响,融入速度细胞和边界细胞以避免移动障碍物对SLAM的影响,利用鼠类混合细胞衍生出的数学模型分析该系统的鲁棒性和实时性。将生物细胞模型引入SLAM,并形成了建模、仿真与实验验证一体化的研究体系,为移动机器人SLAM研究领域多样化提供重要的理论参考。
Abstract:
Aiming at the probabilistic algorithms that have shortcomings such as large computation, high complexity, and failure to find the global optimum, a variety of cells, including border cells, view cells, grid cells and speed cells, are applied to simultaneous localization and mapping (SLAM) in order to construct a BVGSP-SLAM model with multi-celled navigation. A loop closure detection algorithm with keyframe matching is added to SLAM to avoid lighting that changes based on the direction and angle of the light. Speed cells and border cells are added to SLAM to avoid the influence of mobile obstructions. A mathematical model of mixed cells that analyzes robustness and real-time performance of the system is proposed. This project will develop an integrated approach for modeling, simulation and experimental verification, which provide an important theoretical reference on SLAM.

参考文献/References:

[1] TOLMAN E C. Cognitive maps in rats and men[J]. Psychological review, 1948, 55(4): 189-208.
[2] O’KEEFE J, DOSTROVSKY J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat[J]. Brain research, 1971, 34(1): 171-175.
[3] RANCK J L, LETELLIER L, SHECHTER E, et al. X-ray analysis of the kinetics of Escherichia coli lipid and membrane structural transitions[J]. Biochemistry, 1984, 23(21): 4955-4961.
[4] 于乃功, 王琳, 李倜, 等. 网格细胞到位置细胞的竞争型神经网络模型[J]. 控制与决策, 2015, 30(8): 1372-1378.
YU Naigong, WANG Lin, LI Ti, et al. Competitive neural network model from grid cells to place cells[J]. Control and decision, 2015, 30(8): 1372-1378.
[5] 于平, 徐晖, 尹文娟, 等. 网格细胞在空间记忆中的作用[J]. 心理科学进展, 2009, 17(6): 1228-1233.
YU Ping, XU Hui, YIN Wenjuan, et al. The roles of grid cells in spatial memory[J]. Advances in psychological science, 2009, 17(6): 1228-1233.
[6] KROPFF E, CARMICHAEL J E, MOSER M B, et al. Speed cells in the medial entorhinal cortex[J]. Nature, 2015, 523(7561): 419-424.
[7] DEMPSTER A P, LAIRD N M, RUBIN D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the royal statistical society, 1977, 39(1): 1-38.
[8] SMITH R C, CHEESEMAN P. On the representation and estimation of spatial uncertainly[J]. International journal of robotics research, 1987, 5(4): 56-68.
[9] SMITH R, SELF M, CHEESEMAN P. Estimating uncertain spatial relationships in robotics[J]. Machine intelligence and pattern recognition, 1988, 5(5):435-461.
[10] JULIER S J, UHLMANN J K. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE, 2004, 92(3): 401-422.
[11] JULIER S, UHLMANN J, DURRANT-WHYTE H F. A new method for the nonlinear transformation of means and covariances in filters and estimators[J]. IEEE transactions on automatic control, 2000, 45(3): 477-482.
[12] ARASARATNAM I, HAYKIN S. Cubature Kalman filters[J]. IEEE transactions on automatic control, 2009, 54(6): 1254-1269.
[13] NIEDERREITER H. Random number generation and quasi-monte carlo methods[J]. Journal of the american statistical association, 1992, 88(89):147-153.
[14] THRUN S, FOX D, BURGARD W, et al. Robust Monte Carlo localization for mobile robots[J]. Artificial intelligence, 2001, 128(1): 99-141.
[15] MONTEMERLO M, THRUN S, WHITTAKER W. Conditional particle filters for simultaneous mobile robot localization and people-tracking[C]//Proceedings of 2002 IEEE International Conference on Robotics and Automation. Washington, DC, USA, 2002: 695-701.
[16] WEHNER R, GALLIZZI K, FREI C, et al. Calibration processes in desert ant navigation: vector courses and systematic search[J]. Journal of comparative physiology A, 2002, 188(9): 683-693.
[17] 刘新玉, 海鑫, 尚志刚, 等. 利用粒子滤波重建位置细胞编码的运动轨迹[J]. 生物化学与生物物理进展, 2016, 43(8): 817-826.
LIU Xinyu, HAI Xin, SHANG Zhigang, et al. Decoding movement trajectory of hippocampal place cells by particle filter[J]. Progress in biochemistry and biophysics, 2016, 43(8): 817-826.
[18] 胡波, 隋建峰. 海马位置细胞空间信息处理机制的研究进展[J]. 中华神经医学杂志, 2005, 4(4): 416-418.
HU Bo, SUI Jianfeng. Advance of mechanisms of spatial processing for hippocampal place cells[J]. Chinese journal of neuromedicine, 2005, 4(4): 416-418.
[19] 王可, 张婷, 王晓民. 大脑中的“定位系统”——2014年诺贝尔生理学或医学奖简介[J]. 首都医科大学学报, 2014, 35(5): 671-675.
WANG Ke, ZHANG Ting, WANG Xiaomin. “Inner GPS” in the brain——introduction of Nobel Prize in Physiology or Medicine 2014[J]. Journal of capital medical university, 2014, 35(5): 671-675.
[20] 田莉雯. 基于顶部摄像头和鼠载摄像头的大鼠自动导航系统[D]. 杭州: 浙江大学, 2015.
TIAN Liwen. An automatic navigation system based on a rat-mounted camera and a bird’s eye camera[D]. Hangzhou: Zhejiang University, 2015.
[21] 查峰, 肖世德, 冯刘中, 等. 移动机器鼠沿墙导航策略与算法研究[J]. 计算机工程, 2012, 38(6): 172-174.
ZHA Feng, XIAO Shide, FENG Liuzhong, et al. Research on wall-following navigation strategy and algorithm for mobile mechanical mouse[J]. Computer engineering, 2012, 38(6): 172-174.
[22] SKAGGS W E, KNIERIM J J, KUDRIMOTI H S, et al. A model of the neural basis of the rat’s sense of direction[J]. Advances in neural information processing systems, 1995, 7: 173-180.
[23] REDISH A D, ELGA A N, TOURETZKY D S. A coupled attractor model of the rodent head direction system[J]. Network: computation in neural systems, 1997, 7(4): 671-685.
[24] SAMSONOVICH A, MCNAUGHTON B L. Path integration and cognitive mapping in a continuous attractor neural network model[J]. Journal of neuroscience: the official journal of the society for neuroscience, 1997, 17(15): 5900-5920.
[25] STRINGER S M, ROLLS E T, TRAPPENBERG T P, et al. Self-organizing continuous attractor networks and path integration: two-dimensional models of place cells[J]. Network: computation in neural systems, 2002, 13(4): 429-446.
[26] MILFORD M J, WYETH G F, PRASSER D. RatSLAM: a hippocampal model for simultaneous localization and mapping[C]//Proceedings of 2004 IEEE International Conference on Robotics and Automation. New Orleans, LA, USA, 2004: 403-408.
[27] MILFORD M, WYETH G. Persistent navigation and mapping using a biologically inspired SLAM system[J]. International journal of robotics research, 2009, 29(9): 1131-1153.
[28] PRASSER D P, WYETH G, MILFORD M. Experiments in outdoor operation of RatSLAM[C]//Proceedings of 2004 Australasian Conference on Robotics and Automation. Canberra, Australia, 2004: 1-6.
[29] PRASSER D, MILFORD M, WYETH G. Outdoor simultaneous localisation and mapping using RatSLAM[C]//Proceedings of the Results of the 5th International Conference. Berlin, Heidelberg, Germany, 2006: 143-154.
[30] MILFORD M J, PRASSER D, WYETH G. Effect of representation size and visual ambiguity on RatSLAM system performance[C]//Proceedings of 2006 Australasian Conference on Robotics and Automation. Auckland, New Zealand, 2006: 1-8.
[31] MILFORD M, SCHULZ R, PRASSER D, et al. Learning spatial concepts from RatSLAM representations[J]. Robotics and autonomous systems, 2007, 55(5): 403-410.
[32] MILFORD M, WYETH G, PRASSER D. RatSLAM on the edge: revealing a coherent representation from an overloaded rat brain[C]//Proceedings of 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China, 2006: 4060-4065.
[33] DHANDE O S, HUBERMAN A D. Retinal ganglion cell maps in the brain: implications for visual processing.[J]. Current Opinion in Neurobiology, 2014, 24(1): 133.
[34] 许曈, 凌有铸, 陈孟元. 一种融合DGSOM神经网络的仿生算法研究[J]. 智能系统学报, 2017, 12(3):405-412.
XU Tong, LING Youzhu, CHEN Mengyuan. A bio-inspired algorithm integrated with DGSOM neural network[J]. CAAI transactions on intelligent systems, 2017, 12(3):405-412.
[35] MILFORD M J, SCHILL F, CORKE P, et al. Aerial SLAM with a single camera using visual expectation[C]//Proceedings of 2011 IEEE International Conference on Robotics and Automation. Shanghai, China, 2011: 2506–2512.
[36] 张潇, 胡小平, 张礼廉,等. 一种改进的RatSLAM仿生导航算法[J]. 导航与控制, 2015, 14(5):73-79.
ZHANG Xiao, HU Xiaoping, ZHANG Lilian, et al. An improved bionic navigation algorithm based on RatSLAM[J]. Navigation and control, 2015, 14(5): 73-79.
[37] MADDERN W, GLOVER A, GORDON W, et al. Augmenting RatSLAM using FAB-MAP-based visual data association[C]//Curran Associates, 2013:2-4.
[38] 许曈, 凌有铸, 陈孟元, 等. 基于姿态测量模块和闭环检测算法的仿生SLAM研究[J]. 传感技术学报, 2017, 30(6):916-921.
XU Tong, LING Youzhu, CHEN Mengyuan, et al. Bio-inspired SLAM Based on Gesture Measuring and Closed-Loop Detection[J]. Chinese journal of sensors and actuators, 2017, 30(6): 916-921.
[39] BERKVENS R, WEYN M, PEREMANS H. Asynchronous, electromagnetic sensor fusion in RatSLAM[C]//Proceedings of 2015 IEEE SENSORS. Busan, South Korea, 2015: 1-4.

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备注/Memo

备注/Memo:
收稿日期:2017-07-03。
基金项目:安徽高校自然科学研究项目(KJ2016A794).
作者简介:陈孟元,男,1984年生,副教授,博士研究生,主要研究方向为移动机器人地图构建及同步定位。主持安徽省高等学校自然科学研究项目等10余项,发表学术论文30余篇,授权国家发明专利6项。
通讯作者:陈孟元.E-mail:mychen@ahpu.edu.cn.
更新日期/Last Update: 2018-02-01