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

Sensors
Tianyao ZhangGuofeng Zhang

Abstract

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 quantitat...Continue Reading

References

May 29, 2015·Nature·Dario Floreano, Robert J Wood
Oct 13, 2018·Sensors·Roberto OpromollaDomenico Accardo
Oct 9, 2019·Sensors·Roberto OpromollaGiancarmine Fasano
Nov 9, 2019·Sensors·Stamatios SamarasDimitrios Tzovaras
Oct 7, 2020·Science Robotics·Davide FalangaDavide Scaramuzza

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Software Mentioned

COCO
TensorFlow Lite Optimizing Converter ( TOCO )
OpenCV
MATLAB
MobileNet
COCO API
Robot Operating System ( ROS )
OpenCV Android
TensorFlow
DJI Assistant

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