基于BA的改进视觉/惯性融合定位算法

马晓东; 曾碧; 叶林锋

doi:10.12052/gdutxb.170050

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基于BA的改进视觉/惯性融合定位算法

综合研究 | 更新时间：2026-05-27

- 基于BA的改进视觉/惯性融合定位算法
- An Improved Visual Odometry/SINS Integrated Localization Algorithm Based on BA
- 广东工业大学学报 2017年34卷第6期页码：32-36
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  马晓东(1991–)，男,硕士研究生，主要研究方向为计算机视觉、智能机器人
- 基金信息：
  
  广东省产学研专项(2014B090904080);广州市重点科技项目(201604020016);东莞产学研合作成果转化项目(2015509109107)
- DOI：10.12052/gdutxb.170050
  中图分类号： TP24
- 纸质出版：2017
- 稿件说明：
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马晓东, 曾碧, 叶林锋. 基于BA的改进视觉/惯性融合定位算法[J]. 广东工业大学学报, 2017, 34(6): 32-36.

Ma Xiao-dong, Zeng Bi, Ye Lin-feng. An Improved Visual Odometry/SINS Integrated Localization Algorithm Based on BA[J]. 2017, 34(6): 32-36.
马晓东, 曾碧, 叶林锋. 基于BA的改进视觉/惯性融合定位算法[J]. 广东工业大学学报, 2017, 34(6): 32-36. DOI： 10.12052/gdutxb.170050.

Ma Xiao-dong, Zeng Bi, Ye Lin-feng. An Improved Visual Odometry/SINS Integrated Localization Algorithm Based on BA[J]. 2017, 34(6): 32-36. DOI： 10.12052/gdutxb.170050.

摘要

机器人的自主定位是实现导航及智能化的关键. 针对飞行机器人在室内环境中的定位问题

提出一种基于BA的改进视觉/惯性融合定位算法. 该方法首先采用直接法计算获得视觉信息

并结合从惯性单元获得的角速度和加速度信息

用扩展卡尔曼滤波方法进行迭代

提高视觉里程计的鲁棒性; 其次用反向深度法

提高特征点深度信息的估计精度; 最后

通过采用光束平差法(bundle adjustment)进行局部优化. 实验结果表明

本文方法有效提高了机器人定位的精度.

Abstract

The robot's self-localization is the key to realize navigation and intelligent. To deal with the accuracy of location problem of the flying robot in indoor environment

an improved visual odometry/SINS integrated localization algorithm based on BA is proposed. First

the proposed method combines the calculation of visual information by direct method with the angular velocity and acceleration information of inertial unit

and iterate using extended Kalman filter method

which enhances the robustness of the visual odometry. The estimation preciseness of feature points’ depth information is improved with inverse depth method. Then bundle adjustment is used for local optimization. The experiment results show that the proposed algorithm has effectively improved the preciseness of robot-pose estimation.

关键词

Keywords

references

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BURRI M, NIKOLIC J, GOHL P, et al . The EuRoC micro aerial vehicle datasets [J ] . International Journal of Robotics Research, 2016, 35(10): 1157-1163.

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