Temperature Estimation Adaptive to Variables over Distance Using Infrared–LiDAR

Abstract

Measuring accurate surface temperature using a long-wave infrared camera and a non-contact thermometer, is very difficult due to variables such as atmospheric transmittance, emissivity, and influences from the environment such as atmosphere, sun, and dust. Conventional approaches use geometric correction or atmospheric transmittance modeling for temperature correction. However, these approaches have limitations in finding an accurate temperature because it is difficult to fully model a physical phenomenon. In this paper, a new temperature estimation method using distance information of LiDAR and digital count of long-wave infrared camera is proposed. The proposed method estimates the temperature by redefining the mapping function between radiation and digital count by distance. Using the proposed method, if the digital count is measured at a specific distance, accurate temperature can be estimated through the redefined Radiation-Digital count mapping function at a specific distance. The most important property of proposed method is that complex physical modeling is complemented by mapping function of specific distances. In addition, digital counts that change according to the distance at the same temperature required for the mapping function are obtained through linear interpolation using digital count of specific distances. Experimental results using a blackbody, long-wave infrared camera and LiDAR verify that the proposed method estimates the precise temperature. In addition, through experiments on humans, it shows the possibility of accurate body temperature measurement through fusion of long-wave infrared cameras and LiDAR in the future. However, as a limitation, a new calibration is required when the temperature and humidity of the atmosphere change.