Conference item icon

Conference item

End-to-end tracking and semantic segmentation using recurrent neural networks

Abstract:

In this work we present a novel end-to-end framework for tracking and classifying a robot’s surroundings in complex, dynamic and only partially observable real-world environments. The approach deploys a recurrent neural network to filter an input stream of raw laser measurements in order to directly infer object locations, along with their identity in both visible and occluded areas. To achieve this we first train the network using unsupervised Deep Tracking, a recently proposed theoretical f...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

Actions


Access Document


Files:

Authors


Ondruska, P More by this author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Zen Wang, D More by this author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
Pembroke College
Publisher:
Robotics: Science and Systems Publisher's website
Publication date:
2016-06-05
Acceptance date:
2016-04-23
Pubs id:
pubs:820456
URN:
uri:db0b323e-676b-4e6a-98ce-1da54ffc2845
UUID:
uuid:db0b323e-676b-4e6a-98ce-1da54ffc2845
Local pid:
pubs:820456

Terms of use


Metrics



If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP