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移动机器人视觉里程计综述

丁文东 徐德 刘希龙 张大朋 陈天

丁文东, 徐德, 刘希龙, 张大朋, 陈天. 移动机器人视觉里程计综述. 自动化学报, 2018, 44(3): 385-400. doi: 10.16383/j.aas.2018.c170107
引用本文: 丁文东, 徐德, 刘希龙, 张大朋, 陈天. 移动机器人视觉里程计综述. 自动化学报, 2018, 44(3): 385-400. doi: 10.16383/j.aas.2018.c170107
DING Wen-Dong, XU De, LIU Xi-Long, ZHANG Da-Peng, CHEN Tian. Review on Visual Odometry for Mobile Robots. ACTA AUTOMATICA SINICA, 2018, 44(3): 385-400. doi: 10.16383/j.aas.2018.c170107
Citation: DING Wen-Dong, XU De, LIU Xi-Long, ZHANG Da-Peng, CHEN Tian. Review on Visual Odometry for Mobile Robots. ACTA AUTOMATICA SINICA, 2018, 44(3): 385-400. doi: 10.16383/j.aas.2018.c170107

移动机器人视觉里程计综述

doi: 10.16383/j.aas.2018.c170107
基金项目: 

国家自然科学基金 61503376

天津市支持科研院所来津发展项目 16PTYJGX00050

北京市自然科学基金 4161002

国家自然科学基金 51405485

国家自然科学基金 61673383

国家自然科学基金 51405486

详细信息
    作者简介:

    丁文东  中国科学院自动化研究所博士研究生.2013年获得武汉理工大学信息工程学院电子科学与技术学士学位.主要研究方向为视觉测量及定位技术.E-mail:dingwendong2013@ia.ac.cn

    刘希龙  中国科学院自动化研究所副研究员.2009年获得北京交通大学学士学位.2014年获得中国科学院自动化研究所博士学位.主要研究方向为图像处理, 模式识别, 视觉测量.E-mail:xilong.liu@ia.ac.cn

    张大朋  中国科学院自动化研究所副研究员.2003年、2006年获得河北科技大学学士、硕士学位.2011年获得北京航空航天大学博士学位.主要研究方向为机器人视觉测量, 医疗机器人.E-mail:dapeng.zhang@ia.ac.cn

    陈天  中国科学院自动化研究所硕士研究生.2016年获得北京邮电大学学士学位.主要研究方向为视觉定位及三维重建技术.E-mail:chentian2016@ia.ac.cn

    通讯作者:

    徐德  中国科学院自动化研究所研究员.1985年、1990年获得山东科技大学学士、硕士学位.2001年获得浙江大学博士学位.主要研究方向为机器人视觉测量, 视觉伺服, 显微视觉技术.本文通信作者.E-mail:de.xu@ia.ac.cn

Review on Visual Odometry for Mobile Robots

Funds: 

National Natural Science Foundation of China 61503376

the Project of Development in Tianjin for Scientific Research Institutes Supported by Tianjin Government 16PTYJGX00050

Beijing Natural Science Foundation 4161002

National Natural Science Foundation of China 51405485

National Natural Science Foundation of China 61673383

National Natural Science Foundation of China 51405486

More Information
    Author Bio:

     Ph. D. candidate at the Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree in electronic science and technology from Wuhan University of Technology in 2013. His research interest covers visual localization and measurement

     Associate professor at the Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree from Beijing Jiaotong University in 2009, and his Ph. D. degree from the Institute of Automation, Chinese Academy of Sciences in 2014. His research interest covers image processing, pattern recognition, visual measurement, and visual scene cognition

     Associate professor at the Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree and master degree from Hebei University of Science and Technology, in 2003 and 2006, and received his Ph. D. degree from Beijing University of Aeronautics and Astronautics, in 2011. His research interest covers robot vision measurement and medical robot

     Master student at the Institute of Automation, Chinese Academy of Sciences. She received her bachelor degree from Beijing University of Posts and Telecommunications in 2016. Her research interest covers visual localization and reconstruction

    Corresponding author: XU De  Professor at the Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree and master degree from Shandong University of Technology in 1985 and 1990, and received his Ph. D. degree from Zhejiang University in 2001. His research interest covers robot vision measurement, visual servoing, and microvisual technology. Corresponding author of this pape
  • 摘要: 定位是移动机器人导航的重要组成部分.在定位问题中,视觉发挥了越来越重要的作用.本文首先给出了视觉定位的数学描述,然后按照数据关联方式的不同介绍了视觉里程计(Visual odometry,VO)所使用的较为代表性方法,讨论了提高视觉里程计鲁棒性的方法.此外,本文讨论了语义分析在视觉定位中作用以及如何使用深度学习神经网络进行视觉定位的问题.最后,本文简述了视觉定位目前存在的问题和未来的发展方向.
    1)  本文责任编委 侯增广
  • 表  1  直接法与非直接法优缺点对比

    Table  1  The comparison between direct methods and indirect methods

    直接法 非直接法
    目标函数 最小化光度值误差 最小化重投影误差
    优点1 使用了图像中的所有信息 适用于图像帧间的大幅运动
    优点2 使用帧间的增量计算减小了每帧的计算量 比较精确, 对于运动和结构的计算效率高
    缺点1 受限于帧与帧之间的运动比较小的情况 速度慢(计算特征描述等)
    缺点2 通过对运动结构密集的优化比较耗时 需要使用RANSAC等鲁棒估计方法
    容易失败 场景的照明发生变化 纹理较弱的地方
    下载: 导出CSV

    表  2  常用运动模型先验假设

    Table  2  The common used motion model assumption

    运动模型假设 方法
    恒速运动模型 ORB SLAM, PTAM, DPPTAM, DSO
    帧位姿变化为0 DPPTAM, SVO, LSD SLAM
    帧间仿射变换 DT SLAM
    下载: 导出CSV

    表  3  常用的鲁棒估计器

    Table  3  The common used robust estimators

    类型 $\rho(x)$
    $\ell_2$ $\dfrac{x^2}{2}$
    $\ell_1$ $|x|$
    $\ell_1-\ell_2$ $2\left(\sqrt{1+\dfrac{x^2}{2}}-1\right)$
    $\ell_p$ $\dfrac{|x|^\nu}{\nu}$
    Huber $\begin{cases} \dfrac{x^2}{2}, &\mbox{若} \ |x|\leq c\\ c\left(|x|- \dfrac{c}{2}\right), &\mbox{若} \ |x| > c \end{cases}$
    Cauchy $\dfrac{C^2}{2}\ln\left(1+\left( \dfrac{x}{c}\right)^2\right)$
    Tukey $\begin{cases} \dfrac{c^2}{6}\left(1-\left[1-\left( \dfrac{x}{c}\right)^2\right]^3\right), &\mbox{若} |x|\leq c \\ \dfrac{c^2}{6}, &\mbox{若} \ |x| > c \end{cases}$
    t分布 $\dfrac{\nu+1}{\nu+\left( \dfrac{r}{\sigma}\right)^2}$
    下载: 导出CSV

    表  4  VO系统中的鲁棒目标函数设计

    Table  4  The common used robust objection function in VO systems

    VO系统 目标函数框架
    PTAM Tukey biweight
    DSO Huber
    DT SLAM Cauchy distribution
    DPPTAM Reweighted Tukey
    DTAM Weighted Huber norm
    下载: 导出CSV

    表  5  深度图模型

    Table  5  The common used models of depth map

    VO系统 深度模型
    SVO 高斯混合均匀模型
    DSO 高斯模型
    DT SLAM 极线分段约束[61]
    DPPTAM半稠密 一致性假设
    DPPTAM稠密 能量函数1
    DTAM 能量函数2
    LSD SLAM 能量函数3
    1光度值误差+图像空间平滑+平面块假设.
    2使用光度值误差和图像空间平滑(正则).
    3光度值误差和关键帧间方法惩罚.
    下载: 导出CSV

    表  6  深度网络定位系统特点

    Table  6  The comparison of the learning based localization methods

    定位系统 目标函数 输入数据 输出结果 网络类型 面向的问题
    LSM$^{1}$[79] SFA$^{2}$ 2帧图像 位姿 CNN 运动估计
    PoseNet 位姿误差(7) RGB单帧图像 位姿 GoogLeNet$^{3}$ 重定位
    3D-R2N2$^{5}$[72] 体素交叉熵$^{4}$ 单/多帧图像 图像重建 CNN + LSTM 三维重建
    LST$^{6}$[74] 位姿误差 IMU 位姿 LSTM 数据融合
    MatchNet 相似度交叉熵$^{7}$ 2帧图像 匹配度 CNN + FC 图像块匹配
    GVNN 光度值误差 当前/参考图像 位姿 CNN + SE3$^{8}$ 视觉里程计
    HomographNet 图像点误差 2帧图像 单应矩阵 CNN 估计单应矩阵
    SFM-Net[80] 相机运动误差 RGBD图像 相机运动、三维点云 全卷积 相机运动估计和三维重建
    SE3-Net[81] 物体运动误差 点云数据 物体运动 卷积+反卷积 刚体运动
    $^{1}$ Learning to see by moving.
    $^{2}$ SFA使用图像的密集像素匹配目标(损失)函数, 详见文献[82].
    $^{3}$ GoogLeNet是一种22层的CNN网络, 常用于分类识别等.
    $^{4}$体素是一个三维向量, 对应像素(二维), 具有三维坐标, 表示点对应的空间位置的颜色值, 详见文献[83].
    $^{5}$ 3D-R2N2(3D Recurrent reconstruction neural network).
    $^{6}$ Learning to fuse.
    $^{7}$文章在全连接层中使用Softmax层, 因此输出为0/1值, 全连接层输入为拼接的特征点对, 目标函数为sofmax输出值的交叉熵误差.
    $^{8}$除了使用SE3层, 还有包括投影层, 反投影层等.
    下载: 导出CSV

    表  7  视觉定位系统工具库

    Table  7  The common used tools in visual localization

    分类 算法库
    优化 Eigen, g2o[21], ceres[98], GTSAM[99], iSAM[100], SLAM++[101]
    空间变换 Eigen, ROS TF, OpenCV Transform, Sophus
    标定 OpenCV Calib, Kalibr, MATLAB Calibration Toolbox
    特征 OpenCV Feature, VLFeat[102]
    可视化 PCL Visialization, Pangolin, rviz
    SFM Bundler[95], opencvMVG[96], 多视几何Matlab工具箱[97]
    下载: 导出CSV

    表  8  VO系统常用验证数据集

    Table  8  The common used dataset in VO system

    名称 发布时间 数据类型 相机类型 真值来源 传感器 文献
    KITTI VO 2012 png 双目 GPS 激光 [103]
    TUM-Monocular 2012 jpg 单目 [25]
    TUM-RGBD 2012 png + d RGBD [104]
    ICL NUM 2014 png 双目 $^{4}$ [105]
    EuRoC MAV 2016 ROS$^{1}$ + ASL$^{2}$ 双目 VICON$^{3}$ IMU [106]
    Scene Flow 2016 png 双目 $^{4}$ [107]
    COLD$^{5}$ 2009 JPEG 全向$^{6}$ 激光$^{7}$ [108]
    NYU depth 2011/2012 png + d RGBD [109], [110]
    PACAL 3D + 2014 JPEG 单目 $^{4}$ [111]
    $^1$ ROS中使用的一种bag记录文件, 使用ROS可以广播文件中的数据为消息.
    $^2$ ASL为该数据集自定义格式.
    $^3$除了使用VICON另外还有Laser tracker以及3D structure scan, 具体为Vicon motion capture system (6D pose), Leica MS50 laser tracker (3D position), Leica MS50 3D structure scan.
    $^4$合成数据集, 存在真值.
    $^5$这里有一个COLD数据的拓展数据集, 详见http://www.pronobis.pro/data/cold-stockholm
    $^6$系统配备了普通的相机以及全向相机(Omnidirectional camera).
    $^7$除了激光雷达, 还有一个轮式里程计(码盘).
    下载: 导出CSV
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  • 收稿日期:  2017-02-27
  • 录用日期:  2017-09-07
  • 刊出日期:  2018-03-20

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