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首页> 外文期刊>Journal Europeen des Systemes Automatises >Mobile robot localization by Markovian multi models and multisensor fusion approach
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Mobile robot localization by Markovian multi models and multisensor fusion approach

机译：基于马尔可夫模型和多传感器融合的移动机器人定位

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摘要

Fusion and filtering techniques currently' used for the localization of a mobile robot present two main drawbacks. The first one concerns the fact that no a priori reliable information on the input and the measurement noise covariance is generally available. The second one is tied to the fact that the process of localization is often modelled under the form of a unique model leading to the introduction of modelling errors that degrade the quality of the filtering. The work presented in this paper presents two contributions. The first one consists in taking into account the existence of several regimes in the localization process. This one is modelled under the form of a Markovian hybrid process both from state and observation procesess point of view. The second contribution consists in proposing an online adaptative estimation of statistical parameters such as state and observation noise variances along with an optimal management of observations. The fusion of data is performed by Kalman filters of adaptive linear type for linear process and of adaptive extended type for non linear process. This approach has been validated on a robot equipped with an odometer, two telemeters perpendicularly displayed and a gyroscope. In order to show its efficiency, a comparative analysis of its performance with respect to existing approaches is presented.
机译：当前用于移动机器人定位的融合和过滤技术存在两个主要缺点。第一个涉及以下事实：通常没有关于输入和测量噪声协方差的先验可靠信息。第二个问题与以下事实有关：通常以唯一模型的形式对本地化过程进行建模，从而导致引入建模误差，从而降低过滤质量。本文提出的工作提出了两个贡献。第一个是考虑到本地化过程中几种制度的存在。从状态和观察过程的角度来看，这都是以马尔可夫混合过程的形式建模的。第二个贡献在于建议对统计参数（例如状态和观测噪声方差）进行在线自适应估计，以及对观测结果进行最佳管理。数据的融合是通过线性过程的自适应线性类型的卡尔曼滤波器和非线性过程的自适应扩展类型的卡尔曼滤波器执行的。该方法已在配备里程表，垂直显示的两个遥测仪和陀螺仪的机器人上得到验证。为了显示其效率，提出了相对于现有方法的性能比较分析。

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