Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/117299
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Type: Conference paper
Title: Deep learning for 2D scan matching and loop closure
Author: Li, J.
Zhan, H.
Chen, B.
Reid, I.
Lee, G.
Citation: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017 / vol.2017-September, pp.763-768
Publisher: IEEE
Issue Date: 2017
Series/Report no.: IEEE International Conference on Intelligent Robots and Systems
ISBN: 9781538626825
ISSN: 2153-0858
2153-0866
Conference Name: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (24 Sep 2017 - 28 Sep 2017 : Vancouver, Canada)
Statement of
Responsibility: 
Jiaxin Li, Huangying Zhan, Ben M. Chen, Ian Reid, Gim Hee Lee
Abstract: Although 2D LiDAR based Simultaneous Localization and Mapping (SLAM) is a relatively mature topic nowadays, the loop closure problem remains challenging due to the lack of distinctive features in 2D LiDAR range scans. Existing research can be roughly divided into correlation based approaches e.g. scan-to-submap matching and feature based methods e.g. bag-of-words (BoW). In this paper, we solve loop closure detection and relative pose transformation using 2D LiDAR within an end-to-end Deep Learning framework. The algorithm is verified with simulation data and on an Unmanned Aerial Vehicle (UAV) flying in indoor environment. The loop detection ConvNet alone achieves an accuracy of 98.2% in loop closure detection. With a verification step using the scan matching ConvNet, the false positive rate drops to around 0.001%. The proposed approach processes 6000 pairs of raw LiDAR scans per second on a Nvidia GTX1080 GPU.
Rights: © 2017 IEEE
RMID: 0030085606
DOI: 10.1109/IROS.2017.8202236
Appears in Collections:Computer Science publications
Computer Vision publications

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