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Synchronous Adversarial Feature Learning for LiDAR based Loop Closure Detection
Yin P(殷鹏)1,2; He YQ(何玉庆)1; Xu LY(许凌云)1,2; Peng Y(彭艳)3; Han JD(韩建达)1; Xu WL(徐卫良)4
作者部门机器人学研究室
会议名称2018 Annual American Control Conference, ACC 2018
会议日期June 27-29, 2018
会议地点Milwauke, WI, United states
会议录名称2018 Annual American Control Conference, ACC 2018
出版者IEEE
出版地New York
2018
页码234-239
收录类别EI
EI收录号20183605776368
产权排序1
ISSN号0743-1619
ISBN号978-1-5386-5428-6
摘要Loop C losure Detection (LCD) is the essential module in the simultaneous localization and mapping (SLAM) task. In the current appearance-based SLAM methods, the visual inputs are usually affected by illumination, appearance and viewpoints changes. Comparing to the visual inputs, with the active property, light detection and ranging (LiDAR) based point-cloud inputs are invariant to the illumination and appearance changes. In this paper, we extract 3D voxel maps and 2D top view maps from LiDAR inputs, and the former could capture the local geometry into a simplified 3D voxel format, the later could capture the local road structure into a 2D image format. However, the most challenge problem is to obtain efficient features from 3D and 2D maps to against the viewpoints difference. In this paper, we proposed a synchronous adversarial feature learning method for the LCD task, which could learn the higher level abstract features from different domains without any label data. To the best of our knowledge, this work is the first to extract multi-domain adversarial features for the LCD task in real time. To investigate the performance, we test the proposed method on the KITTI odometry dataset. The extensive experiments results show that, the proposed method could largely improve LCD accuracy even under huge viewpoints differences.
语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22737
专题机器人学研究室
通讯作者Yin P(殷鹏)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049
3.School of Mechatronic Engineering and Automation, Robotics, Shanghai, China
4.University of Auckland, Department of Mechanical Engineering, New Zealand
推荐引用方式
GB/T 7714
Yin P,He YQ,Xu LY,et al. Synchronous Adversarial Feature Learning for LiDAR based Loop Closure Detection[C]. New York:IEEE,2018:234-239.
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