Buildings are responsible for approximately 40% of all US energy use and carbon emissions. There exists large potential to improve building efficiency through retro-commissioning, but expense and required expertise of building auditors limit current implementation. Autonomous robotic assessments have the potential to provide consistent building energy audits with reduced cost and enhanced capabilities. As a first step in automating building audits, this paper presents work on automating the lighting analysis of a building.

As an aerial vehicle navigates and explores a room, the prototype system captures images and collects spectrometer readings. These data are used to quantify and classify lighting in a room. Additionally, images acquired from the optical camera are merged to form a composite image of the area. This composite image is used for navigation to lights to record spectrometer readings. Lighting type is then classified from these spectrometer readings. The combined lighting quantification and classification is used to create a topology map of light levels. The combined data are used to perform a thorough analysis of lighting and make lighting recommendations.

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