Automated generation of training sets for object recognition in robotic applications

Abstract

Object recognition plays an important role in robotics, since objects/tools first have to be identified in the scene before they can be manipulated/used. The performance of object recognition largely depends on the training dataset. Usually such training sets are gathered manually by a human operator, a tedious procedure, which ultimately limits the size of the dataset. One reason for manual selection of samples is that results returned by search engines often contain irrelevant images, mainly due to the problem of homographs (words spelled the same but with different meanings). In this paper we present an automated and unsupervised method, coined Trainingset Cleaning by Translation (TCT), for generation of training sets which are able to deal with the problem of homographs. For disambiguation, it uses the context provided by a command like “tighten the nut” together with a combination of public image searches, text searches and translation services. We compare our approach against plain Google image search qualitatively as well as in a classification task and demonstrate that our method indeed leads to a task-relevant training set, which results in an improvement of 24.1% in object recognition for 12 ambiguous classes. In addition, we present an application of our method to a real robot scenario.