원형 패턴을 사용한 강건한 6-DoF추정을 위한 LiDAR 데이터 처리 알고리즘

Abstract

Accurate LiDAR(Light Detection and Ranging) data processing is essential for precise sensor calibration and reliable 3D perception in applications such as autonomous driving and robotics. Traditional methods often rely on artificial targets like circular patterns for 6-DoF estimation due to their ability to precisely detect geometric features. However, few studies focus on improving the accuracy of raw LiDAR data through preprocessing, leaving unresolved issues related to LiDAR’s inherent limitations. To address these challenges, we propose a novel LiDAR data processing algorithm that improves robustness and 6-DoF estimation accuracy. It enhances target plane detection accuracy by using perspective projection, accounting for LiDAR’s range direction errors. We also resolve data bias from scanning patterns using CDR(Centroid Distance Ratio) and directional variance, allowing for the extraction of reliable boundary points. Experimental results demonstrate significantly lower RMSE compared to existing techniques, highlighting the enhanced precision and robustness of our approach. Furthermore, our method achieves higher accuracy in 6-DoF estimation compared to traditional methods.