Semi-supervised online learning for efficient classification of objects in 3D data streams

Abstract

We present a novel learning algorithm especially designed for challenging, large-scale classification problems in mobile robotics. Our method addresses two important aims: first it reduces the required amount of interaction with a human supervisor, which increases the level of autonomy of the learning process. And second, it has the capability to update its internal representation online with every new observed data sample, which makes it adaptive to new environments. The proposed method is based on a combination of two established methods, namely Online Star Clustering and Label Propagation, but it extends and modifies these in such a way that significant shortcomings such as classification inaccuracy and run time inefficiency can be resolved. In experiments on large benchmark data sets, we show that our approach can quickly learn to classify 3D objects with a significantly reduced amount of required ground truth labels for training.