Online Photometric Calibration of Automatic Gain Thermal Infrared Cameras

TL;DR

This paper introduces a novel online photometric calibration algorithm for thermal infrared cameras, enabling improved performance in applications like SLAM and visual odometry without hardware modifications.

Contribution

The paper presents a new calibration method that works with any off-the-shelf thermal IR camera and does not require specialized hardware or driver support.

Findings

Effective calibration demonstrated on benchmark datasets

Improved visual odometry and SLAM performance in outdoor environments

Method applicable to various thermal IR cameras without hardware changes

Abstract

Thermal infrared cameras are increasingly being used in various applications such as robot vision, industrial inspection and medical imaging, thanks to their improved resolution and portability. However, the performance of traditional computer vision techniques developed for electro-optical imagery does not directly translate to the thermal domain due to two major reasons: these algorithms require photometric assumptions to hold, and methods for photometric calibration of RGB cameras cannot be applied to thermal-infrared cameras due to difference in data acquisition and sensor phenomenology. In this paper, we take a step in this direction, and introduce a novel algorithm for online photometric calibration of thermal-infrared cameras. Our proposed method does not require any specific driver/hardware support and hence can be applied to any commercial off-the-shelf thermal IR camera. We present this in the context of visual odometry and SLAM algorithms, and demonstrate the efficacy of our proposed system through extensive experiments for both standard benchmark datasets, and real-world field tests with a thermal-infrared camera in natural outdoor environments.