One-Shot Object Localization Using Learnt Visual Cues Via Siamese Networks

2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)(2019)

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摘要
A robot that can operate in novel and unstructured environments must be capable of recognizing new, previously unseen, objects. In this work, a visual cue is used to specify a novel object of interest which must be localized in new environments. An end-to-end neural network equipped with a Siamese network is used to learn the cue, infer the object of interest, and then to localize it in new environments. We show that a simulated robot can pick-and-place novel objects pointed to by a laser pointer. We also evaluate the performance of the proposed approach on a dataset derived from the Omniglot handwritten character dataset and on a small dataset of toys.
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关键词
simulated robot,pick-place objects,shot object localization,siamese networks,unstructured environments,visual cue,end-to-end neural network,Siamese network,Omniglot handwritten character dataset,laser pointer
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