Appearance-based outdoor localization using group lasso regression
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 …
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 …
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.
The American Society of Mechanical Engineers以上显示的是最相近的搜索结果。 查看全部搜索结果