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Gaussian process estimation of odometry errors for localization and mapping

机译:用于测距误差的高斯过程估计,用于定位和制图

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Since early in robotics the performance of odometry techniques has been of constant research for mobile robots. This is due to its direct influence on localization. The pose error grows unbounded in dead-reckoning systems and its uncertainty has negative impacts in localization and mapping (i.e. SLAM). The dead-reckoning performance in terms of residuals, i.e. the difference between the expected and the real pose state, is related to the statistical error or uncertainty in probabilistic motion models. A novel approach to model odometry errors using Gaussian processes (GPs) is presented. The methodology trains a GP on the residual between the non-linear parametric motion model and the ground truth training data. The result is a GP over odometry residuals which provides an expected value and its uncertainty in order to enhance the belief with respect to the parametric model. The localization and mapping benefits from a comprehensive GP-odometry residuals model. The approach is applied to a planetary rover in an unstructured environment. We show that our approach enhances visual SLAM by efficiently computing image frames and effectively distributing keyframes.
机译:从机器人技术的早期开始,里程表技术的性能就一直在针对移动机器人进行不断研究。这是由于其对本地化的直接影响。姿态误差在死守系统中变得越来越大,其不确定性会对定位和制图(即SLAM)产生负面影响。就残差而言,即期望状态与真实姿势状态之间的差异,推销性能与概率运动模型中的统计误差或不确定性有关。提出了一种使用高斯过程(GPs)对里程计误差进行建模的新颖方法。该方法在非线性参数运动模型和地面真实训练数据之间的残差上训练GP。结果是里程表残差上的GP,该残差提供了期望值及其不确定性,从而增强了对参数模型的信心。本地化和制图得益于完善的GP里程残差模型。该方法适用于非结构化环境中的行星漫游车。我们展示了我们的方法通过有效地计算图像帧和有效地分布关键帧来增强视觉SLAM。

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