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

Huan, Nguyen Van; Choi, Jongeun; Lim, Chae Young; Maiti, Tapabrata

doi:10.1109/aim.2015.7222667

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Although the GSE-based functions V GSE (w) (23) and Φ GSE (θ) (24) accurately approximate the sought-for functions ψ(w) and Φ(θ), respectively, they cannot be considered as the solution to the Robot Localization problem because the mappings g GSE,u (y) and g GSE,v (y) (17), (18) depend on the argument…”

Section: A Splitting Of An Arbitrary Vectormentioning

confidence: 99%

“…Various types of regression between images or their low-dimensional features and robot coordinates are constructed including Gaussian process regression, random forest, etc. [15], [16], [17], [18], [19], [20], [21], [22]. This paper proposes new geometrically motivated machine learning approach to the appearance-based robot localization problem by combining a few advanced techniques.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning in Appearance-Based Robot Self-Localization

Kuleshov

Bernstein

Burnaev

et al. 2017

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)

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An appearance-based robot self-localization problem is considered in the machine learning framework. The appearance space is composed of all possible images, which can be captured by a robot's visual system under all robot localizations. Using recent manifold learning and deep learning techniques, we propose a new geometrically motivated solution based on training data consisting of a finite set of images captured in known locations of the robot. The solution includes estimation of the robot localization mapping from the appearance space to the robot localization space, as well as estimation of the inverse mapping for modeling visual image features. The latter allows solving the robot localization problem as the Kalman filtering problem.

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Section: A Splitting Of An Arbitrary Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning in Appearance-Based Robot Self-Localization

Kuleshov

Bernstein

Burnaev

et al. 2017

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)

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show abstract

“…Preliminary results for indoor and outdoor experiments were reported in Refs. [27] and [28], respectively.…”

Section: Introductionmentioning

confidence: 99%

Appearance-Based Localization of Mobile Robots Using Group LASSO Regression

Huan

Choi

Lim

et al. 2018

Journal of Dynamic Systems, Measurement, and Control

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Appearance-based localization is a robot self-navigation technique that integrates visual appearance and kinematic information. To analyze the visual appearance, we need to build a regression model based on extracted visual features from raw images as predictors to estimate the robot's location in two-dimensional (2D) coordinates. Given the training data, our first problem is to find the optimal subset of the features that maximize the localization performance. To achieve appearance-based localization of a mobile robot, we propose an integrated localization model that consists of two main components: the group least absolute shrinkage and selection operator (LASSO) regression and sequential Bayesian filtering. We project the output of the LASSO regression onto the kinematics of the mobile robot via sequential Bayesian filtering. In particular, we examine two candidates for the Bayesian estimator: the extended Kalman filter (EKF) and particle filter (PF). Our method is implemented in both indoor mobile robot and outdoor vehicle equipped with an omnidirectional camera. The results validate the effectiveness of our proposed approach.

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