SIFT based monocular SLAM with multi-clouds features for indoor navigation

This work introduces a monocular SLAM method, which uses the Scale Invariant Features Transform (SIFT) representation for the scene. The scene represented as clouds of SIFT features within the map. This hierarchical representation of space, serving to estimate the current direction in the environment within the current session. The system exploits the tracking of the same features of successive frames to calculate scalar weights for these features, to build a map of the environment indicating the camera movement, helping the blind persons to navigate more confidently through auditory pathway of their surroundings. EKF is used to estimate the features tracked within the successive frames. The system is tested for using the proposed method with a hand-held camera walking in indoor environment. The results show a good estimation on the spatial locations of the camera within a few milliseconds. The paper shows an electronic cane for navigating in indoor environment using these clouds of features for long-term appearance-based localization of a cane with web camera vision as the external sensor.