Incremental Learning of Visual Landmarks for Mobile Robotics

This paper proposes an incremental scheme for visual landmark learning and recognition. The feature selection stage characterises the landmark using the Opponent SIFT, a color-based variant of the SIFT descriptor. To reduce the dimensionality of this descriptor, an incremental non-parametric discriminant analysis is conducted to seek directions for efficient discrimination (incremental eigenspace learning). On the other hand, the classification stage uses the incremental envolving clustering method (ECM) to group feature vectors into a set of clusters (incremental prototype learning). Then, the final classification is conducted based on the k-nearest neighbor approach, whose prototypes were updated by the ECM. This global scheme enables a classifier to learn incrementally, on-line, and in one pass. Besides, the ECM allows to reduce the memory and computation expenses. Experimental results show that the proposed recognition system is well suited to be used by an autonomous mobile robot.