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.
|Title of host publication||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|
|Publisher||American Society of Mechanical Engineers|
|Publication status||Published - 2015|
|Event||ASME 2015 Dynamic Systems and Control Conference, DSCC 2015 - Columbus, United States|
Duration: 2015 Oct 28 → 2015 Oct 30
|Name||ASME 2015 Dynamic Systems and Control Conference, DSCC 2015|
|Other||ASME 2015 Dynamic Systems and Control Conference, DSCC 2015|
|Period||15/10/28 → 15/10/30|
Bibliographical notePublisher Copyright:
© 2015 by ASME.
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
- Mechanical Engineering
- Control and Systems Engineering