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.
- Dynamic Systems and Control Division
Appearance-Based Outdoor Localization Using Group LASSO Regression
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Do, HN, & Choi, J. "Appearance-Based Outdoor Localization Using Group LASSO Regression." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 3: 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. Columbus, Ohio, USA. October 28–30, 2015. V003T47A003. ASME. https://doi.org/10.1115/DSCC2015-9865
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