Addressing Thermal Camera Variability for Building Thermal Maps
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12061Keywords:
Perception and sensing, Robot navigation, Programming and vision, Sensor integration and perceptionAbstract
The temperature provided by infrared cameras is only an apparent temperature, which should be corrected for multiple factors, including reflected radiation and the emissivity of the observed material. However, the main source of error arises from the camera's own accuracy (or repeatability). This factor, typically ranging between 2% and 5% of the measured value, constitutes the largest error in the measurement process. Although some statistical methods exist to correct this error, they generally entail high computational and time costs. This article proposes a methodology that significantly reduces the residual and random error inherent in thermal cameras, directly improving the quality and consistency of thermal maps composed of multiple thermal images. The method has been successfully tested on a thermal digitization platform comprising a long-range 3D LiDAR and two infrared cameras.
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Copyright (c) 2025 Eduardo Adán, Javier Campos, Adolfo Sánchez, Antonio Adan Oliver
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