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EDPLVO: Efficient Direct Point-Line Visual Odometry

  • Lipu Zhou
  • , Guoquan Huang
  • , Yinian Mao
  • , Shengze Wang
  • , Michael Kaess
  • Meituan
  • University of North Carolina at Chapel Hill
  • Carnegie Mellon University

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

This paper introduces an efficient direct visual odometry (VO) algorithm using points and lines. Pixels on lines are generally adopted in direct methods. However, the original photometric error is only defined for points. It seems difficult to extend it to lines. In previous works, the collinear constraints for points on lines are either ignored [1] or introduce heavy computational load into the resulting optimization system [2]. This paper extends the photometric error for lines. We prove that the 3D points of the points on a 2D line are determined by the inverse depths of the endpoints of the 2D line, and derive a closed-form solution for this problem. This property can significantly reduce the number of variables to speed up the optimization, and can make the collinear constraint exactly satisfied. Furthermore, we introduce a two-step method to further accelerate the optimization, and prove the convergence of this method. The experimental results show that our algorithm outperforms the state-of-the-art direct VO algorithms.

源语言英语
主期刊名2022 IEEE International Conference on Robotics and Automation, ICRA 2022
出版商Institute of Electrical and Electronics Engineers Inc.
7559-7565
页数7
ISBN(电子版)9781728196817
DOI
出版状态已出版 - 2022
已对外发布
活动39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, 美国
期限: 23 5月 202227 5月 2022

出版系列

姓名Proceedings - IEEE International Conference on Robotics and Automation
2022-January
ISSN(印刷版)1050-4729

会议

会议39th IEEE International Conference on Robotics and Automation, ICRA 2022
国家/地区美国
Philadelphia
时期23/05/2227/05/22

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