Appearance-based localization using Group LASSO regression with an indoor experiment

Huan N. Do, Jongeun Choi, C. Lim, T. Maiti
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引用次数: 4

Abstract

This paper proposes appearance-based localization using online vision images collected from an omnidirectional camera attached on a mobile robot or a vehicle. Our approach builds on a combination of the group Least Absolute Shrinkage and Selection Operator (LASSO) and the extended Kalman filter (EKF). Fast Fourier transform (FFT) and Histogram are extracted from omni-directional images, the features of which are selected via the group LASSO regression. The EKF takes the output of the group LASSO regression based first-stage localization as the observation. The indoor experimental results demonstrate the effectiveness of our approach.
基于外观的分组LASSO回归定位与室内实验
本文提出了一种基于外观的定位方法,使用从移动机器人或车辆上的全向相机收集的在线视觉图像。我们的方法建立在最小绝对收缩和选择算子(LASSO)和扩展卡尔曼滤波器(EKF)的组合之上。从全向图像中提取快速傅里叶变换(FFT)和直方图,并通过分组LASSO回归选择特征。EKF将基于分组LASSO回归的第一阶段定位的输出作为观测值。室内实验结果证明了该方法的有效性。
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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