一种移动机器人同时地图绘制和定位的统计方法
A statistical approach to simultaneous mapping and localization for mobile robots
摘要：
移动机器人需要基本的信息来在环境中导航：他们需要知道自己在哪里（定位），他们需要知道自己要去哪里。对于后者，机器人需要一张环境地图。使用各种形式的传感器，机器人在环境中移动时收集信息，以建立地图。本文提出了一种新的采样算法来解决室内环境中的同时地图构建和定位（SLAM）问题。我们从贝叶斯统计的角度来处理这个问题。这些数据对应于在离散时刻获得的一组测距仪和里程计测量值。我们专注于估计的后验分布的空间可能的地图给定的数据。通过利用该分布的不同因子分解，我们推导出三种基于重要性抽样的抽样算法。我们说明了我们的方法的结果，通过机器人导航在卡内基梅隆大学和智利天主教大学的办公楼内获得的两个真实的数据集测试的算法。
英文摘要：
Mobile robots require basic information to navigate through an environment: they need to know where they are (localization) and they need to know where they are going. For the latter, robots need a map of the environment. Using sensors of a variety of forms, robots gather information as they move through an environment in order to build a map. In this paper we present a novel sampling algorithm to solving the simultaneous mapping and localization (SLAM) problem in indoor environments. We approach the problem from a Bayesian statistics perspective. The data correspond to a set of range finder and odometer measurements, obtained at discrete time instants. We focus on the estimation of the posterior distribution over the space of possible maps given the data. By exploiting different factorizations of this distribution, we derive three sampling algorithms based on importance sampling. We illustrate the results of our approach by testing the algorithms with two real data sets obtained through robot navigation inside office buildings at Carnegie Mellon University and the Pontificia Universidad Catolica de Chile.
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