Thermal SLAM: Harnessing Temperature Variations to Enhance Object Detection and Tracking Performance

Thermal Simultaneous Localization and Mapping (SLAM) is a burgeoning field that collects robotics, computer vision, and thermal imaging. In this paper, we tried to present a thorough review of recent advancements in thermal SLAM, with a focus on its role in enhancing object detection and tracking. For better performance in low light, resistance to obstructions, and accuracy in bad weather, thermal SLAM systems work better with visual-based SLAM systems because they use changes in temperature in the environment. The review paper explains the fundamental principles of SLAM, including sensor technologies, data fusion techniques, and mapping algorithms. It then explores the methodologies used for object detection and tracking within the Thermal SLAM framework, encompassing classical approaches and deep learning techniques tailored for thermal imagery analysis. Additionally, the paper discusses challenges and limitations specific to thermal SLAM, such as thermal drift, sensor noise, and calibration issues, while also identifying potential areas for future research. The paper provides a comprehensive survey of applications that utilize thermal SLAM for object detection and tracking across various domains, including autonomous navigation, surveillance, search and rescue operations, and environmental monitoring. It synthesizes case studies and experimental results from relevant literature to demonstrate the effectiveness and practicality of thermal SLAM in complex scenarios where traditional visual-based methods struggle. Overall, this review emphasizes the role of thermal SLAM in advancing autonomous systems and enabling robust object detection and tracking in challenging environments. Examining recent developments, challenges, and applications, it sheds light on the progress made in this field.