Autonomous UAV Location Selection Technique Based on Data Fusion

doi:10.12382/bgxb.2024.0751

Abstract

Abstract:

As a new type of aircraft,the unmanned aerial vehicle (UAV) is gradually integrating into the modern weaponry system and becoming an indispensable and important part of the military field.In order to equip UAV with a safe landing decision-making system that can autonomously perform landing tasks without ground marking,this paper proposes a phased autonomous location selection technique based on multi-sensor data fusion from coarse to fine.The rough landing point search is realized based on the semantic segmentation of the image information.After guiding the UAV to reduce the flight altitude,the terrain parameters are calculated from the elevation value of point cloud information to construct a terrain cost map,and the semantic information of the image is fused by considering the category of a terrain to complete the fine landing point search.The experimental results show that the proposed location selection technique can well delineate the safe and dangerous areas,and enables UAVs to autonomously arrive at a safe landing position.Meanwhile,the comparative analysis of the decision-making in the fine landing point search stage with the fitted point cloud plane verifies that the technique can save decision-making time to a greater extent and improve the efficiency of location selection.

Key words: unmanned aerial vehicle, autonomous landing location selection, data fusion, terrain cost map, semantic segmentation

CLC Number:

TP391

ZHOU Le, YIN Qiaozhi, ZHONG Peilin, WEI Xiaohui, NIE Hong. Autonomous UAV Location Selection Technique Based on Data Fusion[J]. Acta Armamentarii, 2025, 46(8): 240751-.

Figures/Tables 33

Fig.1 Phased UAV landing location selection framework

Fig.2 Visualization results of the model(left :test images,right:predicted results)

Fig.3 Flowchart of image-based landing point search

Fig.4 Construction of terrain cost map

Fig.5 Outdoor simulation environment

Fig.6 UAV model with sensors

Table 1 Performance parameters of vision sensor in simulation

参数	数值
焦距/pixel	639.997649
视场角/(°)	69×42
分辨率	640×480
畸变系数	[0,0,0,0,0]
帧率/(帧·s^-1)	30

Table 2 Performance parameters of LiDAR in simulation

参数	取值
扫描模式	非重复式扫描
测量范围/m	190
视场角/(°)	70.4×77.2
输出/(点·次^-1)	24000
更新频率/Hz	10

Fig.7 Upper-air image

Fig.8 Upper-air semantic image

Fig.9 Binary image

Fig.10 Preliminary result of location selection

Fig.11 The information collected at low altitude

Fig.12 The semantic information at low altitude

Fig.13 The best landing point screened based on plane fitting

Fig.14 The schematic diagrams of the elevation values of point cloud and the terrain parameter grid

Fig.15 Point cloud semantic grid diagram

Fig.16 Terrain cost map

Fig.17 Safe-hazard grid map

Fig.18 Safety distance map

Table 3 Comparison of simulated results in the fine landing point search stage

参数	平面拟合	地形成本图
点云数量/个	225881	225881
最佳点坐标/m	(5.23,-1.07,-18.24)	(6.75,-1.35,-18.24)
运行时间/s	1.1787	0.1437

Fig.19 A real UVA with a location selection system

Table 4 Detailed information of some sensors

设备	类型	参数
视觉传感器	Realsense D455	RGB摄像头分辨率:640×480 视场角:90° × 65° 帧率:30帧/s
激光雷达	Livox avia	量程:190m @10%反射率扫描模式:非重复扫描视场角:70.4°×77.2° 点云输出:240000点/s

Fig.20 Scenario 1 Rough landing point search

Fig.21 Scenario 2 Rough landing point search

Fig.22 Scenario 3 Rough landing point search

Fig.23 Scenario 4 Rough landing point search

Table 5 Real test results in the rough landing point search phase

场景	图像尺寸/pixel	像素坐标/pixel	时间/s
场景1	640×480	(284,344)	0.077
场景2	640×480	(224,344)	0.071
场景3	640×480	(124,264)	0.073
场景4	640×480	(504,124)	0.074

Fig.24 Scenario 1 Fine landing point search

Fig.25 Scenario 2 Fine landing point search

Fig.26 Scenario 3 Fine landing point search

Fig.27 Scenario 4 Fine landing point search

Table 6 Comparison of real test results in the fine landing point search phase

场景	方式	点云坐标/m	时间/ s	时间对比/%
场景1 (105 394个点)	平面拟合	(4.36,-6.15, -22.26)	0.491	72.30
场景1 (105 394个点)	地形成本图	(4.25,-6.55, -22.26)	0.136	72.30
场景2 (84 640个点)	平面拟合	(1.26,-3.74, -19.19)	0.465	72.36
场景2 (84 640个点)	地形成本图	(1.15,-3.85, -19.21)	0.129	72.36
场景3 (89798个点)	平面拟合	(-6.43,-4.94, -20.52)	0.447	73.60
场景3 (89798个点)	地形成本图	(-6.45,-5.65, -20.54)	0.118	73.60
场景4 (84416个点)	平面拟合	(4.48,1.96, -19.51)	0.419	75.42
场景4 (84416个点)	地形成本图	(4.25,2.75, -19.52)	0.103	75.42

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