Appearance-Based Localization of Mobile Robots Using Group LASSO Regression

Huan N. Do, Jongeun Choi, Chae Young Lim, Tapabrata Maiti

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Article number091016
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Issue number9
Publication statusPublished - 2018 Sept 1

Bibliographical note

Funding Information:
National Research Foundation of Korea, Ministry of Science and ICT (2016R1A2B4008237). Ministry of Trade, Industry and Energy, MOTIE, South Korea (Technology Innovation Program 10073129). National Science Foundation (CAREER Award CMMI- 0846547). Vietnam Education Foundation Fellowship. Yonsei University (New Faculty Research and Facility Grant).

Funding Information:
• National Science Foundation (CAREER Award CMMI-0846547).

Funding Information:
• National Research Foundation of Korea, Ministry of Science and ICT (2016R1A2B4008237).

Publisher Copyright:
Copyright © 2018 by ASME.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Instrumentation
  • Mechanical Engineering
  • Computer Science Applications


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