Unsupervised Scene Categorization, Path Segmentation and Landmark Extraction while Traveling Path

Segmenting the movement path is an essential requirement of intelligent mobile robots. It assists intelligent systems in gaining a better understanding of the scene and identifying re-visited spaces. Moreover, it helps intelligent robots quantize the wide scene into sub-spaces that visually represent the same content-for instance, distinguishing rooms and kitchen in an indoor environment. This paper proposes an unsupervised approach to understand the transition of the scene while a robot is moving and helps to extract sub-spaces that visually represent a similar environment. The proposed approach benefits from a pre-trained deep network architecture to extract a description (feature representation) for the mobile robot's visual information at each time step. Then, based on the pairwise distance of the feature representations, sub-spaces of the scene and transition points are extracted.