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基于多关键点检测加权融合的无人机相对位姿估计算法

葛泉波 李凯 张兴国

葛泉波, 李凯, 张兴国. 基于多关键点检测加权融合的无人机相对位姿估计算法. 自动化学报, 2024, 50(7): 1402−1416 doi: 10.16383/j.aas.c230297
引用本文: 葛泉波, 李凯, 张兴国. 基于多关键点检测加权融合的无人机相对位姿估计算法. 自动化学报, 2024, 50(7): 1402−1416 doi: 10.16383/j.aas.c230297
Ge Quan-Bo, Li Kai, Zhang Xing-Guo. Relative pose estimation algorithm for unmanned aerial vehicles based on weighted fusion of multiple keypoint detection. Acta Automatica Sinica, 2024, 50(7): 1402−1416 doi: 10.16383/j.aas.c230297
Citation: Ge Quan-Bo, Li Kai, Zhang Xing-Guo. Relative pose estimation algorithm for unmanned aerial vehicles based on weighted fusion of multiple keypoint detection. Acta Automatica Sinica, 2024, 50(7): 1402−1416 doi: 10.16383/j.aas.c230297

基于多关键点检测加权融合的无人机相对位姿估计算法

doi: 10.16383/j.aas.c230297
基金项目: 国家自然科学基金(62033010), 江苏高校“青蓝工程” (R2023Q07)资助
详细信息
    作者简介:

    葛泉波:南京信息工程大学教授. 主要研究方向为状态估计与信息融合, 目标检测识别与跟踪和自主无人系统与试验测试. 本文通信作者. E-mail: QuanboGe@163.com

    李凯:南京信息工程大学硕士研究生. 主要研究方向为无人机视觉位姿估计, 无人机视觉目标跟踪. E-mail: 20211249528@nuist.edu.cn

    张兴国:中国飞行试验研究院高级工程师. 主要研究方向为飞机试验技术, 智能试验技术. E-mail: zhangxg011@avic.com

Relative Pose Estimation Algorithm for Unmanned Aerial Vehicles Based on Weighted Fusion of Multiple Keypoint Detection

Funds: Supported by National Natural Science Foundation of China (62033010) and Qing Lan Project of Jiangsu Province (R2023Q07)
More Information
    Author Bio:

    GE Quan-Bo Professor at Nanjing University of Information Science and Technology. His research interest covers state estimation and information fusion, object detection recognition and tracking, and autonomous unmanned systems and experimental testing. Corresponding author of this paper

    LI Kai Master student at Nanjing University of Information Science and Technology. His research interest covers unmanned aerial vehicle visual pose estimation and unmanned aerial vehicle visual object tracking

    ZHANG Xing-Guo Senior engineer at China Flight Test Research Institute. His research interest covers aircraft test technology and intelligent test technology

  • 摘要: 针对无人机降落阶段, 因无人船受海面波浪影响对图像产生运动模糊, 导致获取无人机相对位姿精度低且鲁棒性差的问题, 提出一种基于多模型关键点加权融合的6D目标位姿估计算法, 以提高位姿估计的精度和鲁棒性. 首先基于无人船陀螺仪得到的运动信息设计帧间抖动模型, 通过还原图像信息达到降低图像噪声的目的. 然后设计一种多模型的级联回归特征提取算法, 通过多模型检测舰载视觉系统获取的图像, 以增强特征空间的多样性; 同时, 将检测过程中关键点定位形状增量集作为融合权重对模型进行加权融合, 以提高特征空间的鲁棒性. 利用EPnP (Efficient perspective-n-point) 计算关键点相机坐标系坐标, 将PnP (Perspective-n-point) 问题转化为ICP (Iterative closest point) 问题. 最终基于关键点解集的离散度为关键点赋权, 使用ICP算法求解位姿, 以削弱深度信息对位姿的影响. 仿真结果表明, 该算法能够建立一个精度更高的特征空间, 使得位姿解算时特征映射的损失降低, 最终提高位姿解算的精度.
  • 图  1  基于关键点检测的位姿估计框架及其缺点

    Fig.  1  The pose estimation framework based on keypoint detection and its drawbacks

    图  2  运动模糊对纹理的影响

    Fig.  2  Effect of motion blur on texture

    图  3  解决方案

    Fig.  3  Solution

    图  4  算法流程图

    Fig.  4  The flow chart of algorithm

    图  5  实验设备

    Fig.  5  Experimental equipment

    图  6  实验场景

    Fig.  6  Experimental scene

    图  7  关键点检测实验

    Fig.  7  Keypoint detection experiment

    图  8  目标检测实验

    Fig.  8  Object detection experiment

    图  9  关键点类型选择实验

    Fig.  9  Keypoint type selection experiment

    图  10  不同单一模型损失

    Fig.  10  Different single model loss

    图  11  加权融合模型损失

    Fig.  11  Weighted fusion model loss

    图  12  ICP优化(近距离)

    Fig.  12  ICP optimization (close range)

    图  13  ICP优化(远距离)

    Fig.  13  ICP optimization (long range)

    图  14  各算法位姿重构误差

    Fig.  14  Pose reconstruction errors of various algorithms

    表  1  各关键点类型最低检测损失占比

    Table  1  Proportion of minimum detection loss for each keypoint type

    检测关键点类型 角点最优占比 纹理中心点
    最优占比
    选择算法
    最优占比
    纯角点对比选择算法 45.4% 54.6%
    纯纹理中心点
    对比选择算法
    33.3% 66.7%
    下载: 导出CSV

    表  2  各模型均值损失和方差

    Table  2  Mean loss and variance of different models

    模型名称 均值损失 方差
    模型一 0.4001 0.1184
    模型二 0.8213 0.5607
    模型三 0.5640 0.4226
    加权融合模型 0.1401 0.0157
    下载: 导出CSV

    表  3  不同距离中两种ICP算法的均值精度和方差

    Table  3  Mean accuracy and variance of two ICP algorithms in different distances

    距离 算法 均值精度 方差
    近距离(5 m) ICP 87.34% 2.30
    近距离(5 m) 关键点离散度加权的ICP 88.08% 2.06
    远距离(15 m) ICP 83.71% 5.13
    远距离(15 m) 关键点离散度加权的ICP 86.05% 2.98
    下载: 导出CSV

    表  4  不同船体帧间倾角下的各算法位姿估计精度 (%)

    Table  4  Pose estimation accuracy of various algorithms under different ship hull inter-frame angles (%)

    帧间倾角 $ 0^{\circ } $ $ 5^{\circ } $ $ 10^{\circ } $ $ 15^{\circ } $ $ 20^{\circ } $ $ 25^{\circ } $ $ 30^{\circ } $ $ 35^{\circ } $ $ 40^{\circ } $ $ 45^{\circ } $
    本文算法 94.2 93.0 90.4 89.1 88.4 85.3 81.2 82.3 78.3 72.4
    PVNet 93.3 94.7 91.3 82.6 71.3 56.7 45.3 44.0 41.3 36.0
    YOLO-6D 92.0 92.7 88.7 81.3 72.7 63.3 55.3 50.7 46.7 39.3
    传统算法(SIFT模版匹配) 83.4 83.7 81.0 73.8 64.8 54.0 45.3 42.6 39.6 33.9
    下载: 导出CSV
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