Autonomous Robot Navigation with Automatic Learning of Visual Landmarks

By observing visual landmarks it is possible to continuously update an estimated robot position while the robot is moving. In particular, using a triangulation algorithm based on three landmarks the robot position can be estimated each time three landmarks are observed. If the selected landmark triplet is an optimal triplet, the estimated robot position is accurate and errors do not accumulate. In order to make the system autonomous, both acquisition and observation of landmarks have to be carried out automatically. The paper consequently proposes a method for learning and navigation of a working environment. In particular, a two-phase procedure is proposed: rst phase is for an automatic acquisition of visual-landmarks, second phase is for estimating robot position during navigation. Since visual landmarks acquired during the rst phase have to be recognized during the second phase, a method is proposed for accurate landmark acquisition. In this way, the recognition will be tolerant to positional errors and changes in viewpoint. Feasibility and applicability of the proposed method is based on a system with a simple setup. Novelty and potentiality, are in combining algorithms for optimal landmark-triplet selection, triangulation and robust landmark acquisition.