A Target-Based and A Targetless Extrinsic Calibration Methods for Thermal Camera and 3D LiDAR
Abstract
This thesis introduces two novel methods for the extrinsic calibration of a thermal camera and a 3D LiDAR sensor, which are crucial for seamless data integration. The first method employs a distinctive calibration target, leveraging lines and plane equations correspondence in both modalities for a single pose, and incorporating more poses by matching the target's edges. It achieves reliable results, even with just one pose yielding 10.82% translation and 0.51-degree rotation errors. This outperforms alternative methods, which require eight pairs for similar results. The second method eliminates the need for a dedicated target. Instead, by collecting data during the sensor setup movement in environment and using a novel evolutionary algorithm optimizes a loss that measures alignment of humans in both modalities. This approach results in a 4.43% loss improvement compared to extrinsic parameters obtained by target-based methods. These methods save calibration time, reduce costs, and make sensor integration more accessible.