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Error modeling of various sensors for robotics application using Allan Variance technique

机译:使用Allan Variance技术对机器人应用中的各种传感器进行误差建模

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Present day mobile robots are meant for very precise applications. For very precise applications of mobile robots, accurate estimation of inertial parameters depends upon the accuracy of mathematical model & as well as accuracy (error characteristics) of the individual sensor measurements. Multiple sensors add redundancies to the system as well as it helps to estimate the system states accurately through judicious fusion. As sensor measurements itself are prone to various types of noises, the detail error modeling is very essential for estimation of appropriate signals from the noisy sensor data. The essence of the error modeling is to understand & characterize the different types of noises present in the measured. This paper illustrates the characterization and identification of noises present in the Encoder (Model: Maxon HEDL-5540) & Inertial navigation system (Model: Crossbow NAV440) measurements using Allan Variance technique.
机译:当今的移动机器人适用于非常精确的应用。对于移动机器人的非常精确的应用,惯性参数的准确估计取决于数学模型的准确性以及各个传感器测量的准确性(误差特性)。多个传感器为系统增加了冗余,并且通过明智的融合有助于准确估计系统状态。由于传感器测量本身容易产生各种类型的噪声,因此细节误差建模对于从嘈杂的传感器数据中估计适当的信号非常重要。误差建模的本质是理解和表征测量中存在的不同类型的噪声。本文说明了使用Allan Variance技术对编码器(型号:Maxon HEDL-5540)和惯性导航系统(型号:Crossbow NAV440)测量中存在的噪声进行表征和识别。

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