A Survey on Vision-based Sensing and Pose Estimation Methods for UAV Autonomous Landing
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摘要: 自主着陆技术是制约无人机 (Unmanned aerial vehicle, UAV) 自主性等级提升中极具挑战性的一项技术. 立足于未来基于视觉的无人机自主着陆技术的发展需求, 围绕其中的核心问题——着陆场检测与位姿估计, 对近十年来国内外无人机自主着陆领域中基于视觉的着陆场检测与位姿估计方法研究进展进行总结. 首先, 在分析无人机自主着陆应用需求的基础上, 指出机器视觉在无人机自主着陆领域的应用优势, 并凝练出存在的科学问题; 其次, 按不同应用场景划分对着陆场检测算法进行梳理; 然后, 分别对纯视觉、多源信息融合的位姿估计技术研究成果进行归纳; 最后, 总结该领域有待进一步解决的难点, 并对未来的技术发展趋势进行展望.Abstract: Autonomous landing technology is the most challenging technology to restrict the improvement of unmanned aerial vehicle (UAV) autonomy level. Focusing on the core problem of autonomous landing technology of UAV——Landing site detection and pose estimation, the research progress in this field in the past decade is summarized in this paper, which is on the basis of the development demand of vision-based autonomous landing technology in the future. First of all, on the basis of analyzing the application demand for UAV autonomous landing, the advantages of the application of machine vision in the field of UAV autonomous landing are pointed out, and the existing scientific problems are summarized. Secondly, the landing site detection algorithms are sorted according to different application scenarios. Then, the research progress of pose estimation technology based on vision and multi-information fusion are summarized, respectively. Finally, the challenges to be solved in this field are summarized, and future technical development trends are introduced.
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图 2 几种机器视觉传感器的测量原理示意图
Fig. 2 Schematic diagram of measuring principles of several machine vision sensors
表 1 FAA精密进近与着陆标准
Table 1 The FAA precision approach and landing standard
着陆分级 决断高度(m) 水平精度(m) 垂直精度(m) 角度容差(%) I类 60 9.1 3.0 7.5 II类 30 4.6 1.4 4.0 III类 15 4.1 0.5 4.0 
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表 2 几种机器视觉传感器的测量原理及特点
Table 2 Measurement principles and characteristics of several machine vision sensors
传感器类型 测量方式 测量原理 有效测量范围(m) 测量精度 ToF 主动视觉 通过红外发射器发射调制过的光脉冲, 再由接收器接收遇到目标后反射回来的光脉冲, 并根据光脉冲的往返时间计算与目标之间的距离 $ 0.1 \sim 10 $ 厘米级 结构光 主动视觉 通过红外激光器, 将具有一定结构特征的光线投射到被拍摄物体上, 再由专门的红外摄像头进行采集反射的结构光图案, 根据三角测量原理进行深度信息的计算 $ 0.1 \sim 6 $ 毫米级 双目(立体) 视觉 被动视觉 不需要主动对外发射光源, 通过左右两个摄像头获取图像信息, 解算视差得出目标的位置和深度信息 $ 0.3 \sim 25 $ 厘米级 单目视觉 被动视觉 不需要主动对外发射光源, 通过对单目摄像头获取的图像信息进行增强、目标检测及跟踪、位姿估计等一系列的图像算法处理, 间接地获取目标的相对位姿信息 $0.3 \sim 20\, 000$ —
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表 3 典型的着陆场检测方法总结
Table 3 A summary of typical landing site detection methods
检测
方案文献 着陆场
类型着陆场检测方法 特殊应用场景 实验结果 移动
平台背景
复杂不完全
成像光照
变化面向
合作
标识基于
典型
特征[33] “H”形 仿射不变矩和Harris 角点检测 $\checkmark$ $\checkmark$ 平均检测精度为95% [36] “H”形 基于DLS 的椭圆拟合方法 $\checkmark$ 平均检测精度为92% [37] “H”形 基于分层特征和特征金字塔的改进
YOLOv3-tiny 网络模型$\checkmark$ 平均检测精度为85.88%, 平均检测速度为17 Hz [38] “H”形 融合高低层特征的改进SSD 网络模型 $\checkmark$ $\checkmark$ 平均检测精度为85.88%, 平均检测速度为17 Hz [41] “H”形 基于深度残差网络和特征金字塔的改进
SSD 网络模型$\checkmark$ $\checkmark$ 平均检测精度约为78% [34] “T”形 Canny 边缘检测与Hu 矩匹配 平均检测速度为33 Hz [35] 3D标识 Canny 和SURF 特征 $\checkmark$ 平均检测误差约为3 px, 平均检测速度为 63 Hz [39] 矩形 YOLOv3-tiny 网络模型和改进TLD $\checkmark$ $\checkmark$ 平均检测精度为98.5%, 平均检测速度为53 Hz [42] 圆形 VGG-M 模型与主动强化学习 $\checkmark$ 平均检测误差为13.55 px 图像
分割[49] 圆形 自动阈值分割算法 $\checkmark$ $\checkmark$ 平均检测精度为66% [50] 圆形 HSV 分割和Canny 边缘检测 $\checkmark$ $\checkmark$ 平均检测误差约为5 px [51] 圆形 LLIE-Net 图像增强网络模型和基于
FastMBD的图像分割$\checkmark$ 平均检测精度为88%, 平均检测速度为21 Hz [52] 矩形 改进的ERFNet 网络模型 $\checkmark$ 平均检测精度为76.35%, 平均检测速度为45 Hz 基准
标识
系统[44] AprilTag 基于ROI 和CMT 的改进AprilTag 算法 $\checkmark$ 自主降落的精度为0.3 m [43] AprilTag 两级级联的改进MobileNet 网络模型 $\checkmark$ 平均检测精度为98%, 平均检测速度为 31 Hz [45] AprilTag 基于ROI 的AprilTag 算法 $\checkmark$ 平均检测精度为100% [47] AprilTag 基于局部搜索和调整分辨率策略的改进
AprilTag 识别算法$\checkmark$ 平均检测速度为20 Hz [48] AprilTag AprilTag 编码和阈值法 $\checkmark$ $\checkmark$ $\checkmark$ 平均检测精度为98.8% [46] ArUco HOG 特征和基于TLD 框架的改进KCF 算法 $\checkmark$ 平均检测精度为82.24%, 平均
检测速度为31.47 Hz面向
跑道基于
几何
特征[55] 舰基跑道 EDline 线特征检测 $\checkmark$ 平均检测精度为65.5% [57] 舰基跑道 形态学特征和边缘检测 $\checkmark$ 平均检测误差约为7 px [53] 陆基跑道 基于LSD 的改进FDCM 边缘检测 平均检测误差约为10 px, 平均检测速度为 13 Hz [54] 陆基跑道 YOLOv3 剪枝模型和概率Hough 变换 $\checkmark$ $\checkmark$ 平均检测精度约70%, 平均检测速度为16 Hz [56] 陆基跑道 频域残差法与SIFT 特征 $\checkmark$ 平均检测精度为94% [58] 陆基跑道 基于分割的区域竞争和特定能量函数最小化策略 $\checkmark$ $\checkmark$ 平均检测误差约为8 px, 平均检测速度
不低于20 Hz[59] 陆基跑道 SIFT 特征与CSRT 跟踪 $\checkmark$ 平均检测精度为94.89%, 平均检测速度为4.3 Hz 基于
深度
特征[61] 陆基跑道 基于角点回归的改进YOLOv3 网络模型 平均检测精度为98.3%, 平均检测速度为25 Hz [62] 陆基跑道 RunwayNet 网络模型 平均检测精度为90%
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表 4 典型的单目视觉位姿估计方法梳理
Table 4 A summary of typical monocular vision-based pose estimation methods
文献 位姿估计方法 测量范围 实验结果 独立帧 [65] 正交迭代法 500 m飞行半径内 最大位置估计误差为5 m, 最大姿态估计误差为$ 2^\circ $ [66] Powell＇s-Dogleg 算法 3 m飞行半径内 三轴平均位置误差分别为17.95 cm、11.50 cm、3.65 cm,
三轴平均姿态误差分别为$ 8.43^\circ $、$ 9.11^\circ $、$ 0.56^\circ $[67] POSIT 算法 400 m飞行半径内 三轴平均位置误差分别为0.5 m、0.8 m、2.5 m, 偏航角平均误差为$ 0.1^\circ $ [68] 单应矩阵分解 3 m飞行半径内 位置均方根误差为0.0138 m, 三轴姿态均方根误差分别为
$ 1.98^\circ $、$ 1.41^\circ $、$ 0.22^\circ $[69] Ma.Y.B 编码与L-M 算法 2 m飞行半径内 俯仰角、滚转角和偏航角的平均误差分别为$ 0.36^\circ $、$ 0.40^\circ $、$ 0.38^\circ $ [70] L-M 算法 2 km飞行半径内 平均位置估计误差小于10 m, 平均姿态估计误差小于$ 2^\circ $ [71] 基于圆和角点特征的相对位姿
估计改进算法20 m 高度范围内 水平位置和高度平均误差分别为0.005 m、0.054 m,
偏航角平均误差为$ 1.6^\circ $[72] 目标中心约束与最小二乘法 1 m高度范围内 平均位置误差小于20 mm, 平均姿态误差小于$ 0.5^\circ $ [74] L-M 与单状态卡尔曼滤波 250 m飞行半径内 平均位置误差不超过0.7 m [78] 最小二乘法 60 m高度范围内 最大位置估计误差为6.52 m, 最大姿态估计误差为$ 0.08^\circ $ 连续帧 [79] 多点观测法 10 m 高度范围内 平均位置误差为1.47 m, 平均姿态误差为$ 1^\circ $ [80] SURF 与单应性分解 0.72 m飞行半径内 最大位置估计误差小于0.05 m [81] SPoseNet 网络模型 80 m飞行半径内 位置均方根误差小于7 m, 偏航角均方根误差小于$ 6^\circ $ [82] 基于运动点剔除的优化算法 120 m飞行半径内 平均位置误差约为2 cm [83] P4P 及UKF 0.26 m高度范围内 平均位置估计误差为2.4% [84] BA 3 m高度范围内 平均位置误差为4 mm
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表 5 不同信息融合层级的特点
Table 5 Characteristics of different information fusion levels
像素级融合 特征级融合 位姿级融合 信息损失 最小 中等 最大 对传感器的依赖性 最大 中等 最小 算法复杂度 最高 中等 最低 系统开放性 最低 中等 最高
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表 6 典型的视觉/惯性融合位姿估计方法梳理
Table 6 A summary of typical pose estimation methods based on visual-inertial fusion
文献 信息源 融合方法 测量范围 实验结果 滤波 [87] 视觉/惯性 卡尔曼滤波和Mean Shift 4 m飞行半径内 速度估计均方根误差为0.04 m/s, 位置估计均方根误差为0.02 m [88] 视觉/惯性/高度计 卡尔曼滤波 100 m高度范围内 平均位置估计误差小于0.4 m, 平均姿态估计误差小于$ 1^\circ $ [89] 视觉/惯性 EKF滤波 500 m飞行半径内 平均位置估计误差小于2 m [90] 视觉/惯性 SR_UKF滤波 150 m飞行半径内 平均位置估计误差为0.0531 m, 平均姿态估计误差为$0.020\,3^\circ$ [91] 视觉/惯性 UKF滤波 距离甲板约5 m内 平均位置估计误差为0.23 m, 平均姿态估计误差为$ 5^\circ $ [92] 视觉/惯性/气压计 ESKF滤波 20 m高度范围内 平均位置测量误差小于5 m, 速度误差小于2 m/s [93] 视觉/惯性/雷达 联邦滤波器 距离甲板400 m内 纵向平均位置估计误差为1.6 m, 横侧向平均位置
估计误差为0.8 m, 航向角平均估计误差为$ 0.1^\circ $[94] 视觉/惯性 歧义校正算法 30 m飞行半径内 平均位置估计误差约2 cm [95] 视觉/惯性 改进的粒子滤波 0.4 m飞行半径内 平均位置估计误差约0.87 cm [96] 视觉/惯性 时延滤波器 800 m 飞行半径内 最大位置估计误差为15 m 优化 [98] 视觉/惯性 流形优化 4 m高度范围内 平均位置误差小于0.2 m [99] 视觉/惯性/
激光测距仪因子图优化 300 m高度范围内 平均位置估计误差约3 m 深度
学习[102] 视觉/惯性 ResNet18 和LSTM 20 m飞行半径内 平均位置估计误差为0.08 m [103] 视觉/惯性 CNN和BiLSTM 160 m飞行半径内 平均高度估计误差小于1.0567 m [104] 视觉/惯性 FlowNet和LSTM 3 m高度范围内 平均位置估计误差为0.28 m, 平均姿态估计误差为$ 38^\circ $
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