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Yu-Cheng Zhang Ya-Wen Chang Jin-Siang Shaw

Abstract

In recent years, global warming has heightened
energy conservation awareness. Taiwan's Ministry of Economic
Affairs reports a record-high total electricity consumption in 2021
within the past decade, with lighting and air conditioning being the
biggest energy consumers in commercial and office spaces.
Employing YOLOv4 deep learning, we've developed a character
recognition system for precise identification in experiments.
Statistical data shows an average 228-minute electricity savings
within 1800 minutes, reducing consumption by 12.7%. To create a
privacy-conscious lighting energy-saving system, we've forsaken
RGB cameras in favor of 3D LiDAR (Light Detection and Ranging)
for point cloud image construction and processing under the
Robot Operating System (ROS). Ultimately, individuals within the
environment are selected from the point cloud and managed based
on indoor lighting configurations.

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Keywords

Point Cloud Filtering, Rviz, 3D Point Cloud, Automation, ROS

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