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Stabilize an Unsupervised Feature Learning for LiDAR-based Place Recognition
Yin P(殷鹏)1,2; Xu LY(许凌云)1,2; Liu Z(刘哲)3; Li, Lu4; Salman, Hadi4; He YQ(何玉庆)1,2; Xu WL(徐卫良)5; Wang HS(王贺升)6; Choset, Howie4
Department机器人学研究室
Conference Name2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Conference DateOctober 1-5, 2018
Conference PlaceMadrid, Spain
Source Publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Publication PlaceNew York
2018
Pages1162-1167
Indexed ByEI ; CPCI(ISTP)
EI Accession number20191206649878
WOS IDWOS:000458872701042
Contribution Rank1
ISSN2153-0858
ISBN978-1-5386-8094-0
AbstractPlace recognition is one of the major challenges for the LiDAR-based effective localization and mapping task. Traditional methods are usually relying on geometry matching to achieve place recognition, where a global geometry map need to be restored. In this paper, we accomplish the place recognition task based on an end-to-end feature learning framework with the LiDAR inputs. This method consists of two core modules, a dynamic octree mapping module that generates local 2D maps with the consideration of the robot's motion; and an unsupervised place feature learning module which is an improved adversarial feature learning network with additional assistance for the long-term place recognition requirement. More specially, in place feature learning, we present an additional Generative Adversarial Network with a designed Conditional Entropy Reduction module to stabilize the feature learning process in an unsupervised manner. We evaluate the proposed method on the Kitti dataset and North Campus Long-Term LiDAR dataset. Experimental results show that the proposed method outperforms state-of-the-art in place recognition tasks under long-term applications. What's more, the feature size and inference efficiency in the proposed method are applicable in real-time performance on practical robotic platforms.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/23864
Collection机器人学研究室
Corresponding AuthorXu LY(许凌云)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong
4.Robotics Institute at Carnegie Mellon University, Pittsburgh, USA
5.Department of Mechanical Engineering, University of Auckland, New Zealand
6.Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Recommended Citation
GB/T 7714
Yin P,Xu LY,Liu Z,et al. Stabilize an Unsupervised Feature Learning for LiDAR-based Place Recognition[C]. New York:IEEE,2018:1162-1167.
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