Appearance-Based Localization of Mobile Robots Using Group LASSO Regression

Abstract

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.