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|Title:||Deep learning for 2D scan matching and loop closure|
|Citation:||2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017 / vol.2017-September, pp.763-768|
|Series/Report no.:||IEEE International Conference on Intelligent Robots and Systems|
|Conference Name:||IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (24 Sep 2017 - 28 Sep 2017 : Vancouver, Canada)|
|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|
|Appears in Collections:||Computer Science publications|
Computer Vision publications
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