Autonomous acquisition of generic handheld objects in unstructured environments via sequential back-tracking for object recognition

Robots operating in human environments must have the ability to autonomously acquire object representations in order to perform object search and recognition tasks without human intervention. However, autonomous acquisition of object appearance model in an unstructured and cluttered human environment is a challenging task, since the object boundaries are unknown in prior. In this paper, we present a novel method to solve the problem of unknown object boundaries for handheld objects in an unstructured environment using robotic vision. The objective is to solve the problem of object segmentation without prior knowledge of the objects that human interacts with daily. In particular, we present a method that segments handheld objects by observing human-object interaction process, and performs incremental learning on the acquired models using SVM. The unknown object boundary is estimated using sequential back-tracking via exploitation of affine relationship of consecutive frames. The segmentation is achieved using identified optimal object boundaries, and the extracted models are used to perform future object search and recognition tasks.