Visual landmark extraction and recognition for autonomous robot navigation

Abstract

The robot navigation using visual landmark approach is described. The landmarks are not preselected or otherwise defined a priori, but rather, they are extracted automatically during a learning phase. To facilitate this, a saliency map is constructed which highlights potential landmarks. This is used in conjunction with a qualitative segregation of the workspace, to further delineate the search areas for environment landmarks. For the sake of robustness, no semantic information is attached to the landmarks; they are stored as raw patterns along with information readily available from the workspace segregation, that facilitates their accurate rate recognition at a later, navigation session. During such a session, similar steps with the learning phase are employed to locate landmarks. The stored information is used to transform a previously leaned landmark pattern, according to the current position of the observer, achieving thus accurate landmark recognition. Results obtained from our approach demonstrate its validity and applicability in indoor workspaces.