Bayesian estimation and Kalman filtering: a unified framework for mobile robot localization

S. Roumeliotis, G. Bekey
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引用次数: 198

Abstract

Decision and estimation theory are closely related topics in applied probability. In this paper, Bayesian hypothesis testing is combined with Kalman filtering to merge two different approaches to map-based mobile robot localization; namely Markov localization and pose tracking. A robot carries proprioceptive sensors that monitor its motion and allow it to estimate its trajectory as it moves away from a known location. A single Kalman filter is used for tracking the pose displacements of the robot between different areas. The robot is also equipped with exteroceptive sensors that seek for landmarks in the environment. Simple feature extraction algorithms process the incoming signals and suggest potential corresponding locations on the map. Bayesian hypothesis testing is applied in order to combine the continuous Kalman filter displacement estimates with the discrete landmark pose measurement events. Within this framework, also known as multiple hypothesis tracking, multimodal probability distribution functions can be represented and this inherent limitation of the Kalman filter is overcome.
贝叶斯估计和卡尔曼滤波:移动机器人定位的统一框架
决策理论和估计理论是应用概率论中密切相关的两个主题。本文将贝叶斯假设检验与卡尔曼滤波相结合,融合了两种不同的基于地图的移动机器人定位方法;即马尔可夫定位和姿态跟踪。机器人携带的本体感觉传感器可以监控其运动,并允许它在离开已知位置时估计其轨迹。单个卡尔曼滤波用于跟踪机器人在不同区域之间的姿态位移。该机器人还配备了外感传感器,可以在环境中寻找地标。简单的特征提取算法处理输入的信号,并在地图上建议潜在的对应位置。为了将连续的卡尔曼滤波位移估计与离散的地标姿态测量事件相结合,采用贝叶斯假设检验。在这个框架内,也称为多假设跟踪,可以表示多模态概率分布函数,克服了卡尔曼滤波器的固有局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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