A Machine Learning Method for Vision-Based Unmanned Aerial Vehicle Systems to Understand Unknown Environments

What makes unmanned aerial vehicles (UAVs) intelligent is their capability of sensing and understanding new unknown environments. Some studies utilize computer vision algorithms like Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) to sense the environment for pose estim...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-06, Vol.20 (11), p.3245
Main Authors: Zhang, Tianyao, Hu, Xiaoguang, Xiao, Jin, Zhang, Guofeng
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Automation
color detection
Color imagery
Computer vision
Embedded systems
Image detection
Machine learning
object detection
Odometers
real-time
Robots
Sensors
Simultaneous localization and mapping
Smartphones
UAV
Unknown environments
Unmanned aerial vehicles
visual RGB
Working conditions
YOLOv3
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Summary:What makes unmanned aerial vehicles (UAVs) intelligent is their capability of sensing and understanding new unknown environments. Some studies utilize computer vision algorithms like Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) to sense the environment for pose estimation, obstacles avoidance and visual servoing. However, understanding the new environment (i.e., make the UAV recognize generic objects) is still an essential scientific problem that lacks a solution. Therefore, this paper takes a step to understand the items in an unknown environment. The aim of this research is to enable the UAV with basic understanding capability for a high-level UAV flock application in the future. Specially, firstly, the proposed understanding method combines machine learning and traditional algorithm to understand the unknown environment through RGB images; secondly, the You Only Look Once (YOLO) object detection system is integrated (based on TensorFlow) in a smartphone to perceive the position and category of 80 classes of objects in the images; thirdly, the method makes the UAV more intelligent and liberates the operator from labor; fourthly, detection accuracy and latency in working condition are quantitatively evaluated, and properties of generality (can be used in various platforms), transportability (easily deployed from one platform to another) and scalability (easily updated and maintained) for UAV flocks are qualitatively discussed. The experiments suggest that the method has enough accuracy to recognize various objects with high computational speed, and excellent properties of generality, transportability and scalability.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20113245