Semantic web-mining and deep vision for lifelong object discovery
Jay Young, Lars Kunze, Valerio Basile, Elena Cabrio, Nick Hawes, Barbara Caputo
Abstract
Autonomous robots that are to assist humans in their daily lives must recognize and understand the meaning of objects in their environment. However, the open nature of the world means robots must be able to learn and extend their knowledge about previously unknown objects on-line. In this work we investigate the problem of unknown object hypotheses generation, and employ a semantic Web-mining framework along with deep-learning-based object detectors. This allows us to make use of both visual and semantic features in combined hypotheses generation. Experiments on data from mobile robots in real world application deployments show that this combination improves performance over the use of either method in isolation.
BibTeX
@inproceedings{icra2017_semanticwebminin,
title = {Semantic web-mining and deep vision for lifelong object discovery},
author = {Jay Young and Lars Kunze and Valerio Basile and Elena Cabrio and Nick Hawes and Barbara Caputo},
booktitle = {ICRA 2017},
year = {2017}
}