小型无人机视觉传感器避障方法综述

doi:10.19665/j.issn1001-2400.20241008

Abstract

Abstract:

The UAV autonomous flight obstacle avoidance technology is one of the most fundamental and critical technologies for the safe flight and application of drones,and it is also a current research hotspot in the UAV field.With the application of deep learning in computer vision and the rapid development and continuous improvement of visual sensors such as event cameras,methods about UAV autonomous flight obstacle avoidance based on visual sensors have made some progress.However,many existing research methods still face significant challenges in complex scenarios and a series of urgent problems that need to be addressed,particularly in terms of accuracy,real-time performance,and algorithm robustness.This paper first introduces the relevant concepts and difficulties of UAV obstacle avoidance.Then it categorizes obstacle avoidance algorithms based on visual sensors according to the hardware and technical approaches used into traditional obstacle avoidance methods,deep learning-based obstacle avoidance methods,event stream processing-based obstacle avoidance methods,sensor fusion-based obstacle avoidance methods,and decision-level obstacle avoidance methods based on vision.Each category of obstacle avoidance methods is discussed in detail,including their research progress and achievements,as well as an analysis of their advantages and disadvantages.Finally,the paper summarizes the existing problems in UAV obstacle avoidance algorithms and provides an outlook on future research directions.

Key words: unmanned aerial vehicle, obstacle detectors, computer vision, event camera

CLC Number:

TP249

WANG Jialiang, DONG Kai, GU Zhaojun, CHEN Hui, HAN Qiang. Review of obstacle avoidance methods for small UAVs using visual sensors[J].Journal of Xidian University, 2025, 52(1): 60-79.

Figures/Tables 18

参考文献	实现方法	适用场景	网络输出	数据集	实验平台
文献[68]	CNN	室内	无人机指令	无人机随机飞行直至碰撞的飞行数据	Parrot AR.Drone
文献[56]	CNN	室内	深度图	NYU-V2数据集、使用Kinect 传感器收集的办公室数据集ESAT	Parrot Bebop
文献[69]	CNN+YOLO +DSO	森林小路	3个偏移量和3个方向概率、目标检测结果、深度图	IDSIA森林步道数据集、 3个摄像头收集的数据	NVIDIA Jetson TX1
文献[75]	CNN	森林	距离障碍物的3种情况	Gazebo模拟环境数据	Parrot Bebop 2.0
文献[72]	CNN	室内走廊	无人机指令	无人机在走廊飞行收集的数据	Parrot AR.Drone
文献[70]	CNN	室外道路	碰撞概率、偏转角	在自行车车把上安装 GoPro相机收集的数据	Parrot Bebop 2.0
文献[62]	CNN	室外	目标检测结果	ImageNet-2102数据集	NVIDIA Jetson TX2 + 1080Ti
文献[73]	双流CNN	室内	3个方向上的距离	将3对超声波和红外距离传感器安装在无人机上收集的数据	Parrot AR.Drone
文献[56]	CNN	室外	深度图	MegaDepth数据集	Parrot Bebop 2.0
文献[65]	Fester R-CNN	对人避障	目标检测结果		机载Raspberry Pi 3b+处理器
文献[60]	CNN	室外森林	与障碍物距离3种情况	Gazebo仿真环境中收集的数据	Parrot Bebop 2.0
文献[63]	SSD+双目测距	室外	目标检测结果	从扑翼飞行器的航空摄影中收集两小时的视频图像	扑翼飞行器(机载 Raspberry Pi 3b+处理器)
文献[71]	CNN	室外	碰撞概率、转向角	Comma.ai、Udacity的数据集	Parrot Bebop 2.0
文献[55]	RL、LSTM、 cGAN、RNN	室内	深度图、无人机指令	Gazebo仿真环境中收集的数据	仿真
文献[64]	Faster R-CNN	人工树林	目标检测结果	无人机飞行收集的数据	Parrot Bebop 2.0
文献[74]	CNN	室外	3条轨迹及对应的碰撞代价	Flightmare模拟器收集的数据	NVIDIA Jetson TX2
文献[66]	FastDepth+ NanoDet	室内外	深度图、目标检测结果	COCO2017	Parrot Bebop 2.0
文献[67]	YOLO	室内外	目标检测结果	收集的网球图像数据	NVIDIA Jetson NX

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	尺度不变性	旋转不变性	算法实时性
Harris	否	是	慢
FAST	否	否	快
SIFT	是	是	较慢
SURF	是	是	较快
ORB	是	是	快

传感器类型	探测距离/m	影响性能因素	适用场景
超声波	<10	受气温等环境因素影响	探测距离较小、成本较低和指向性强
红外	<50	强光、水雾	可靠性要求较高
毫米波雷达	<250	受环境影响较少	可靠性要求高、作业环境复杂度高
单目	<10	光照强度过强或过弱	成本较低、对光强有一定要求、有其他避障传感器补充
双目	<100	光照强度过强或过弱	成本较低、对光强有一定要求

Review of obstacle avoidance methods for small UAVs using visual sensors

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距离	尺寸/m
距离	≤0.5	≤0.1	≤1.5
≤0.1	92	90	88
≤ 0.2	87	92	97
≤ 0.3	81	88	93

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