[1]伍 明,孙继银.一种机器人未知环境下动态目标跟踪交互多模滤波算法[J].智能系统学报,2010,5(2):127-138.
WU Ming,SUN Ji-yin.An interacting multiple model filtering algorithm for mobile robots to improve tracking of moving objects in unknown environments[J].CAAI Transactions on Intelligent Systems,2010,5(2):127-138.
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WU Ming,SUN Ji-yin.An interacting multiple model filtering algorithm for mobile robots to improve tracking of moving objects in unknown environments[J].CAAI Transactions on Intelligent Systems,2010,5(2):127-138.
一种机器人未知环境下动态目标跟踪交互多模滤波算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
5
期数:
2010年第2期
页码:
127-138
栏目:
学术论文—机器感知与模式识别
出版日期:
2010-04-25
- Title:
- An interacting multiple model filtering algorithm for mobile robots to improve tracking of moving objects in unknown environments
- 文章编号:
- 1673-4785(2010)02-0127-12
- Keywords:
- interacting multiple model filter; extended Kalman filter; simultaneous localization and mapping; object tracking; mobile robot
- 分类号:
- TP242.6
- 文献标志码:
- A
- 摘要:
- 为了解决机器人同时定位、地图构建和目标跟踪问题，提出了一种基于交互多模滤波(interacting multiple model filter, IMM)的方法.该方法将机器人状态、目标状态和环境特征状态作为整体来构成系统状态向量并利用全关联扩展式卡尔曼滤波算法对系统状态进行估计，由此随着迭代估计的进行，系统各对象状态之间将产生足够的相关性，这种相关性能够正确反映各对象状态估计间的依赖关系，因此提高了目标跟踪的准确性.该方法进一步和传统的IMM滤波算法相结合，从而解决了目标运动模式未知性问题，IMM方法的采用使系统在完成目标追踪的同时还能对其运动模态进行估计，进而提高了该算法对于机动目标的跟踪能力.仿真实验验证了该方法对机器人和目标的运动轨迹以及目标运动模态进行估计的准确性和有效性
- Abstract:
- A novel method was developed for synchronous localization and mapping (SLAM) and object tracking (OT) to provide simultaneous estimation of a robot’s and any object’s trajectories in an unknown environment. The system was based on interacting multiple model (IMM) filtering. In this approach, the states of robots, objects and landmarks were used to form an integrated system state. A full covariance extended Kalman filter (EKF) was then employed to estimate system state. As the iterative estimation progressed, sufficient correlations between the different objects in the system could be establish to reflect the interdependent relationships of estimations between different system objects. In this way the precision of object state estimation was improved. Moreover, when combined with a traditional IMM filter algorithm, this method solved the uncertainty problem for modes of object motion. With the application of IMM, the method helped robots to track objects and estimate their modes of motion, improving the accuracy of object localization. Simulation results validated the effectiveness of the proposed method in the estimation of the trajectories of robots and objects and the modes of motion of tracked targets.
- 参考文献/References:
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备注/Memo
收稿日期：2009-08-30.
通信作者：伍 明.E-mail:hyacinth531@163.com.
作者简介：
伍 明，男，1981年生，博士研究生，主要研究方向为智能移动机器人技术、多机器人协作.
孙继银，男，1952年生，教授、博士生导师，中国计算机学会高级会员，中国计算机用户协会理事.全军先进教育工作者，多项科研成果获全军科技进步奖.发表学术论文70余篇.
更新日期/Last Update:
2010-05-24