Abstract
Integrating multiple types of sensors into autonomous systems, such as cars and robots, has become a widely adopted approach in modern technology. Among these sensors, RGB cameras, thermal cameras, and LiDAR are particularly valued for their ability to provide comprehensive environmental data. However, despite their advantages, current research primarily focuses on the one or combination of two sensors at a time. The full potential of utilizing all three sensors is often neglected. One key challenge is the ego-motion compensation of data in dynamic situations, which results from the rotational nature of the LiDAR sensor, and the blind spots of standard cameras due to their limited field of view. To resolve this problem, this paper proposes a novel method for the simultaneous registration of LiDAR, panoramic RGB cameras, and panoramic thermal cameras in dynamic environments without the need for calibration targets. Initially, essential features from RGB images, thermal data, and LiDAR point clouds are extracted through a novel method, designed to capture significant raw data characteristics. These extracted features then serve as a foundation for ego-motion compensation, optimizing the initial dataset. Subsequently, the raw features can be further refined to enhance calibration accuracy, achieving more precise alignment results. The results of the paper demonstrate the effectiveness of this approach in enhancing multiple sensor calibration compared to other ways. In the case of a high speed of around 9 m/s, some situations can improve the accuracy about 30 percent higher for LiDAR and Camera calibration. The proposed method has the potential to significantly improve the reliability and accuracy of autonomous systems in real-world scenarios, particularly under challenging environmental conditions.