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)

Abstract

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
DOIs
Publication statusPublished - 2015 Jan 1
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
Volume3

Other

OtherASME 2015 Dynamic Systems and Control Conference, DSCC 2015
CountryUnited States
CityColumbus
Period15/10/2815/10/30

Fingerprint

Extended Kalman filters
Cameras

All Science Journal Classification (ASJC) codes

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

Cite this

Do, H. N., & Choi, J. (2015). Appearance-based outdoor localization using group lasso regression. In Multiagent 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 (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015; Vol. 3). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2015-9865
Do, Huan N. ; Choi, Jongeun. / Appearance-based outdoor localization using group lasso regression. Multiagent 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. American Society of Mechanical Engineers, 2015. (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015).
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abstract = "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.",
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Do, HN & Choi, J 2015, Appearance-based outdoor localization using group lasso regression. in Multiagent 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. ASME 2015 Dynamic Systems and Control Conference, DSCC 2015, vol. 3, American Society of Mechanical Engineers, ASME 2015 Dynamic Systems and Control Conference, DSCC 2015, Columbus, United States, 15/10/28. https://doi.org/10.1115/DSCC2015-9865

Appearance-based outdoor localization using group lasso regression. / Do, Huan N.; Choi, Jongeun.

Multiagent 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. American Society of Mechanical Engineers, 2015. (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015; Vol. 3).

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

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N2 - 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.

AB - 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.

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Do HN, Choi J. Appearance-based outdoor localization using group lasso regression. In Multiagent 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. American Society of Mechanical Engineers. 2015. (ASME 2015 Dynamic Systems and Control Conference, DSCC 2015). https://doi.org/10.1115/DSCC2015-9865