基于稀疏直接法闭环检测定位的视觉里程计

汝少楠; 何元烈; 叶星余

doi:10.12052/gdutxb.200111

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基于稀疏直接法闭环检测定位的视觉里程计

更新时间：2026-05-27

- 基于稀疏直接法闭环检测定位的视觉里程计
- Visual Odometry Based on Sparse Direct Method Loop-Closure Detection
- 广东工业大学学报 2021年38卷第3期页码：48-54
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  汝少楠(1997–)，男，硕士研究生，主要研究方向为计算机视觉、多传感器
  何元烈(1976–)，男，副教授，主要研究方向为计算机视觉、深度学习和智能机器人，E-mail：heyuanlie@163.com
- 基金信息：
  
  国家自然科学基金资助项目(61876043)
- DOI：10.12052/gdutxb.200111
  中图分类号： TP249
- 网络首发：2021-05-10，
  
  纸质出版：2021
- 稿件说明：
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汝少楠, 何元烈, 叶星余. 基于稀疏直接法闭环检测定位的视觉里程计[J]. 广东工业大学学报, 2021, 38(3): 48-54.

Ru Shao-nan, He Yuan-lie, Ye Xing-yu. Visual Odometry Based on Sparse Direct Method Loop-Closure Detection[J]. 2021, 38(3): 48-54.
汝少楠, 何元烈, 叶星余. 基于稀疏直接法闭环检测定位的视觉里程计[J]. 广东工业大学学报, 2021, 38(3): 48-54. DOI： 10.12052/gdutxb.200111.

Ru Shao-nan, He Yuan-lie, Ye Xing-yu. Visual Odometry Based on Sparse Direct Method Loop-Closure Detection[J]. 2021, 38(3): 48-54. DOI： 10.12052/gdutxb.200111.

摘要

视觉里程计在移动机器人的定位导航中发挥着关键性作用

但当前的算法在运行速度、轨迹精度和鲁棒性等方面依然存在改善空间。为提高相机轨迹精度

提出基于稀疏直接法的闭环检测算法。该算法直接提取两种特征组成混合型特征点提升系统鲁棒性

混合型特征点用于跟踪和匹配关键帧

使视觉里程计能够检测闭环

再用位姿图优化提升定位精度。实验结果表明在复杂环境中具有较强的鲁棒性

并且在速度和精度之间取得平衡。

Abstract

The visual odometry plays a key role in the positioning and navigation of mobile robots

but the current algorithm still has room for improvement in terms of running speed

trajectory accuracy

and robustness. In order to improve the accuracy of camera trajectory

a loop-closure detection algorithm is proposed based on the sparse direct method. The algorithm directly extracts two features to form a hybrid feature point to improve the robustness of the system. The hybrid feature point is used to track and match key frames

so that the visual odometry can detect closed loops

and then the pose map is used to optimize the positioning accuracy. Experimental results show strong robustness in a complex environment and a balance between speed and accuracy.

关键词

Keywords

references

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