Appearance-based outdoor localization using group lasso regression

Huan N. Do, Jongeun Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


This paper presents appearance-based localization for an omni-directional camera that builds on a combination of the group Least Absolute Shrinkage and Selection Operator (LASSO) and the extended Kalman filter (EKF). A histogram that represents the population of the Speeded-Up Robust Features (SURF points) is computed for each image, the features of which are selected via the group LASSO regression. The EKF takes the output of the LASSO regression-based first localization as observations for the final localization. The experimental results demonstrate the effectiveness of our approach.

Original languageEnglish
Title of host publicationMultiagent Network Systems; Natural Gas and Heat Exchangers; Path Planning and Motion Control; Powertrain Systems; Rehab Robotics; Robot Manipulators; Rollover Prevention (AVS); Sensors and Actuators; Time Delay Systems; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamics Control; Vibration and Control of Smart Structures/Mech Systems; Vibration Issues in Mechanical Systems
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791857267
Publication statusPublished - 2015
EventASME 2015 Dynamic Systems and Control Conference, DSCC 2015 - Columbus, United States
Duration: 2015 Oct 282015 Oct 30

Publication series

NameASME 2015 Dynamic Systems and Control Conference, DSCC 2015


OtherASME 2015 Dynamic Systems and Control Conference, DSCC 2015
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2015 by ASME.

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Mechanical Engineering
  • Control and Systems Engineering


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