Classification and identification of robot sensing data based on nested infinite GMMs

This paper demonstrates some experimental proofs of the model for the classification and identification of robot sensing data. Autonomous robots are equipped with varied sensors to assist them in understanding and interacting with their environments. In contrast to traditional model approaches that are based on the Gaussian assumption, we propose the application of the infinite Gaussian mixture model (iGMM) to detect known and unknown data. Two key components are denoted: 1) simultaneous training of the number of classes and dimensions of each model, and 2) infinite modeling to adjust for observations that do not match with previous knowledge.