SIFT-based monocluar SLAM with inverse depth parameterization for robot localization

Abstract

We have developed a monocular SLAM method which uses the scale-invariant feature transform (SIFT) algorithm to detect salient features within the scene. Only feature points with large scales are considered as worth-tracking features to reduce the computation load and enhance the robustness. These feature information are input to an extended Kalman filter with the spatial coordinates of the feature points and that of the observing camera as its state variables. The angular and translational velocity and acceleration of the camera are also included as the state variables.