Active global localization for a mobile robot using multiple hypothesis tracking
Active global localization for a mobile robot using multiple hypothesis tracking
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DOI：
10.1109/70.964673
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发表时间：
2001-10-01
期刊：
影响因子：
--
通讯作者：
Kristensen, S
中科院分区：
文献类型：
--
作者：
Jensfelt, P;Kristensen, S
在本文中，我们提出了一种利用不完整拓扑世界模型进行移动机器人定位的概率方法。我们将这种方法称为多假设定位（MHL），它使用基于多假设卡尔曼滤波器的位姿跟踪，并结合假设正确性的概率公式，在线生成和跟踪高斯位姿假设。除了计算复杂度较低之外，这种方法相对于传统的基于网格的方法具有优势，可以利用不完整的拓扑世界模型信息。此外，该方法为平台生成运动指令，以增强位姿估计过程中的信息收集。我们展示了在两种不同环境（一个典型的办公环境和一个旧医院建筑）中进行的大量实验。
In this paper we present a probabilistic approach for mobile robot localization using an incomplete topological world model. The method, which we have termed multi-hypothesis localization (MHL), uses multi-hypothesis Kalman filter based pose tracking combined with a probabilistic formulation of hypothesis correctness to generate and track Gaussian pose hypotheses online. Apart from a lower computational complexity, this approach has the advantage over traditional grid based methods that incomplete and topological world model information can be utilized. Furthermore, the method generates movement commands for the platform to enhance the gathering of information for the pose estimation process. Extensive experiments are presented from two different environments, a typical office environment and an old hospital building.