Consistent cooperative localization
Consistent cooperative localization
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DOI：
10.1109/robot.2009.5152859
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发表时间：
2009-05
期刊：
影响因子：
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通讯作者：
A. Bahr;Matthew R. Walter;J. Leonard
中科院分区：
文献类型：
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作者：
A. Bahr;Matthew R. Walter;J. Leonard
在协作导航中，移动机器人团队获得彼此的距离和/或角度测量以及航位推算信息，以帮助彼此更准确地导航。一种典型的方法是移动基线导航，其中多个自主水下航行器（auv）使用声学调制解调器交换距离测量值来执行移动三边测量。虽然车辆之间的信息共享可能非常有益，但交换测量值和状态估计也可能是危险的，因为测量值有可能被车辆多次使用；这样的数据重用导致不一致（过度自信）的估计，使数据关联和异常值拒绝更加困难，更有可能出现分歧。在本文中，我们提出了一种多auv执行移动三边定位的一致协同定位技术。每个AUV都建立了一个过滤器库，执行仔细的记录来跟踪测量的来源，并防止使用任何测量值超过一次。多个估计以一致的方式组合，产生保守的协方差估计。仿真结果说明了该技术的可行性。将新方法与不跟踪测量原点的朴素方法进行对比，说明新方法保持保守的协方差边界，而朴素方法得到的状态估计过于自信和发散。
In cooperative navigation, teams of mobile robots obtain range and/or angle measurements to each other and dead-reckoning information to help each other navigate more accurately. One typical approach is moving baseline navigation, in which multiple Autonomous Underwater Vehicles (AUVs) exchange range measurements using acoustic modems to perform mobile trilateration. While the sharing of information between vehicles can be highly beneficial, exchanging measurements and state estimates can also be dangerous because of the risk of measurements being used by a vehicle more than once; such data re-use leads to inconsistent (overconfident) estimates, making data association and outlier rejection more difficult and divergence more likely. In this paper, we present a technique for the consistent cooperative localization of multiple AUVs performing mobile trilateration. Each AUV establishes a bank of filters, performing careful bookkeeping to track the origins of measurements and prevent the use any of the measurements more than once. The multiple estimates are combined in a consistent manner, yielding conservative covariance estimates. The technique is illustrated using simulation results. The new method is compared side-by-side with a naive approach that does not keep track of the origins of measurements, illustrating that the new method keeps conservative covariance bounds whereas state estimates obtained with the naive approach become overconfident and diverge.