移动机器人中单目视觉里程计的研究

池鹏可; 苏成悦

doi:10.12052/gdutxb.160122

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移动机器人中单目视觉里程计的研究

综合研究 | 更新时间：2026-05-27

- 移动机器人中单目视觉里程计的研究
- A Research on Monocular Visual Odometry for Mobile Robots
- 广东工业大学学报 2017年34卷第5期页码：40-44
- 作者机构：
- 作者简介：
  
  池鹏可(1991-),男,硕士研究生,主要研究方向为机器人控制技术、机器视觉.
  苏成悦(1961-),男,教授,博士,主要研究方向为应用物理、机器视觉.E-mail:scy.gdut@163.com
- 基金信息：
  
  广东省信息产业发展专项现代信息服务业项目（2150510）
- DOI：10.12052/gdutxb.160122
  中图分类号： TP242
- 网络首发：2017-09-09，
  
  纸质出版：2017
- 稿件说明：
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池鹏可, 苏成悦. 移动机器人中单目视觉里程计的研究[J]. 广东工业大学学报, 2017, 34(5): 40-44.

Chi Peng-ke, Su Cheng-yue. A Research on Monocular Visual Odometry for Mobile Robots[J]. 2017, 34(5): 40-44.
池鹏可, 苏成悦. 移动机器人中单目视觉里程计的研究[J]. 广东工业大学学报, 2017, 34(5): 40-44. DOI： 10.12052/gdutxb.160122.

Chi Peng-ke, Su Cheng-yue. A Research on Monocular Visual Odometry for Mobile Robots[J]. 2017, 34(5): 40-44. DOI： 10.12052/gdutxb.160122.

摘要

将特征法与直接法相结合用于实现移动机器人的单目视觉里程计.采用Shi-Tomasi检测算法确保FAST检测算法提取的特征点的稳定性，通过四叉树算法使特征点分布均匀，并使用金字塔KLT算法跟踪下一帧图像的特征点.通过归一化直接线性变换DLT算法计算的单应矩阵重构相关位姿，利用最小化光度测量误差方法对特征点图像块进行直接匹配来估计移动机器人的运动位姿，并利用g2o库优化局部连续的运动位姿信息，提高运动估计的鲁棒性.通过TurtleBot移动机器人在室内环境下进行了实验验证，利用KITTI数据集测试本文设计的单目视觉里程计，实验结果表明本文所提方法的有效性和实用性.

Abstract

A mobile robot monocular visual odometry based on feature-based method and direct method is proposed. The Shi-Tomasi detector algorithm is used to improve the stability of feature points extracted by FAST algorithm

and the quadtree algorithm is used to ensure uniform distribution of feature points

which may be beneficial to using pyramid KLT algorithm to track these feature points of the next frame image. And then the relative pose of camera is recovered by the homography matrix that is computed by the normalized direct linear transformation algorithm. Finally

the photometric error between image patches is minimized to estimate the movement pose of the mobile robot and the optimization framework g2o is used to optimize local continuous position and orientation information that can improve the robustness of motion estimation. The proposed monocular visual odometry is tested on the TurtleBot platform in indoor room and the KITTI dataset. The experimental results have verified the effectiveness and practicability of the proposed approach.

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references

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