PL-LIT: A LiDAR-Inertial-Thermal SLAM Using Point-Line Features and Thermographic Mapping

2026-06-28

AI Summary

This work addresses the challenge that thermal imaging, while advantageous in adverse conditions such as strong illumination, low light, or fog, violates the brightness constancy assumption due to automatic gain control, thereby degrading conventional photometric visual odometry. To overcome this limitation, the authors propose a tightly coupled LiDAR–inertial–thermal SLAM system that incorporates online photometric calibration and leverages a deep neural network to extract point and line features. The approach further integrates reliability-aware line feature constraints, an error-state iterative Kalman filter (ESIKF), and a probabilistic thermal intensity voxel map. By eliminating reliance on brightness constancy, the method achieves robust performance in both visible and thermal-infrared environments, demonstrates state-of-the-art accuracy on long-range thermal datasets, and enables real-time thermal anomaly detection for practical safety inspection applications.

Abstract

Thermal imaging is resilient to adverse conditions, such as intense illumination, low-light operation, and fog, and can therefore mitigate odometry degradation when visible-spectrum imagery becomes unreliable. Nevertheless, most thermal cameras employ automatic gain control (AGC), and thermal images often present low global contrast despite containing informative edge structures. These characteristics undermine brightness constancy and cause conventional optical flow tracking-based odometry pipelines that fundamentally rely on the brightness constancy assumption across consecutive frames. To address these issues, we propose a general LiDAR-Inertial-Thermal SLAM system that accommodates both visible-light and thermal cameras. PL-LIT combines an online photometric calibration module with a deep neural network for point-line feature extraction, enabling more stable and repeatable thermal tracking. For state estimation, we design a tightly coupled LiDAR-Inertial-Thermal formulation within an Error-State Iterated Kalman Filter (ESIKF). We further introduce a line-feature constraint scheme ensuring the reliability of geometric constraints across varying thermal appearances. In addition, PL-LIT builds a probabilistic thermal-intensity voxel map, which supports real-time thermal anomaly detection. Extensive experiments demonstrate that PL-LIT exhibits generality and robustness in visible-light environments, achieves state-of-the-art performance on long-range thermal infrared datasets, and provides practical safety inspection functionality based on thermographic mapping.

Problem

• thermal imaging
• brightness constancy
• visual odometry
• SLAM
• low-light conditions

Innovation

• LiDAR-Inertial-Thermal SLAM
• point-line feature extraction
• online photometric calibration
• thermal-intensity voxel map
• Error-State Iterated Kalman Filter