Autonomous–Targetless Extrinsic Calibration of Thermal, RGB, and LiDAR Sensors

Mobile robots extensively employ multiple sensors, including RGBD cameras, LiDAR, and thermal sensors. Sensor fusion plays a vital role in localization and environment perception tasks. However, traditional manual target-based methods for achieving consistent alignment among multiple sensors, especially thermal cameras, are laborious and lack adaptability. This work introduces an autonomous–targetless framework for calibrating LiDAR–RGB and LiDAR–thermal sensors in mobile robots. In our proposed framework, we examine the characteristics of thermal images, identify suitable calibration scenarios, and employ the thermal bridge and line-based PnL algorithm to enable autonomous and targetless calibration. Experimental results demonstrate the efficiency of our method, with overall translation errors of 2.77 and 3.86 cm, and overall rotation errors of 0.21° and 0.46°, respectively, in LiDAR–RGB and LiDAR–thermal calibrations. These results are comparable to state-of-the-art techniques and traditional target-based manual methods. The analysis of different thermal scenes highlights the importance of well-aligned and distinguishable edges across thermal–RGB–LiDAR modalities for optimal calibration results. Simulation tests using synthetic data and validation tests using real-world data showcase the robustness of our model in executing targetless extrinsic calibrations.