Improving the precision on multi robot localization by using a series of filters hierarchically distributed
Improving the precision on multi robot localization by using a series of filters hierarchically distributed
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DOI：
10.1109/iros.2007.4399043
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发表时间：
2007-12
期刊：
影响因子：
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通讯作者：
Agostino Martinelli
中科院分区：
文献类型：
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作者：
Agostino Martinelli
本文介绍了一种新的方法，同时本地化的一队移动的机器人配备本体感受传感器能够监测他们的运动，并与exteroceptive传感器能够感知对方的问题。该方法是基于一系列的扩展卡尔曼滤波器分层分布。特别是，该团队被分解成几个组，对于每个组，扩展卡尔曼滤波器估计的配置的所有成员的组中的一个本地框架连接到一个机器人，组长。最后，在层次结构的最高级别，一个单一的过滤器估计所有组长的位置。这种方法的主要优点是它能够分配必要的计算，以执行有限的计算和通信能力下的多机器人定位。特别是，该方法显着优于基于单个估计的最佳方法。这是通过分析计算精度的情况下，一个单自由度的每个机器人的定位。特别是，最好的层次分析确定的通信和计算能力和传感器的精度上的定位精度的依赖关系。
This paper introduces a new approach to the problem of simultaneously localizing a team of mobile robots equipped with proprioceptive sensors able to monitor their motion and with exteroceptive sensors able of sensing one another. The method is based on a series of extended Kalman filters hierarchically distributed. In particular, the team is decomposed in several groups and, for each group, an extended Kalman filter estimates the configurations of all the members of the group in a local frame attached to one robot, the group leader. Finally, at the highest level of the hierarchy, one single filter estimates the locations of all the group leaders. The key advantage of this approach is its ability to distribute the computation necessary to perform the multi robot localization under limited computation and communication capabilities. In particular, the approach significantly outperforms an optimal approach based on a single estimator. This is shown by analytically computing the precision on the localization of each robot in the case of one single degree of freedom. In particular, the best hierarchy is analytically determined by deriving the dependency of the localization precision on the communication and computation capabilities and on the sensors accuracy.