Extrinsic calibration for multi-LiDAR systems involving heterogeneous laser scanning models

In the realm of autonomous driving, there is a pressing demand for heightened perceptual capabilities, giving rise to a plethora of multisensory solutions. Among these, multi-LiDAR systems have gained significant popularity. Within the spectrum of available combinations, the integration of repetitive and non-repetitive LiDAR configurations emerges as a balanced approach, offering a favorable trade-off between sensing range and cost. However, the calibration of such systems remains a challenge due to the diverse nature of point clouds, low-common-view, and distinct densities. This study proposed a novel targetless calibration algorithm for extrinsic calibration between Hybrid-Solid-State-LiDAR(SSL) and Mechanical-LiDAR systems, each employing different scanning modes. The algorithm harnesses planar features within the scene to construct matching costs, while proposing the adoption of the Gaussian Mixture Model (GMM) to address outliers, thereby mitigating the issue of overlapping points. Dynamic trust-region-based optimization is incorporated during iterative processes to enhance nonlinear convergence speed. Comprehensive evaluations across diverse simulated and real-world scenarios affirm the robustness and precision of our algorithm, outperforming current state-of-the-art methods.