Extraction of Corresponding Point Cloud Features Based on Least Squares Deviation Analysis

Accurate extraction of corresponding feature points between adjacent frames is crucial for improving pose estimation accuracy in multi-line LiDAR frame-to-frame registration. This paper proposes a least squares deviation analysis method to optimize the matching process of pole-like object features. First, point cloud data is preprocessed, including denoising and grid projection, to enhance feature point stability. Then, candidate feature points are initially selected using point cloud clustering and bounding box methods. The least squares method is applied to analyze the deviation of features between adjacent frames, eliminating points with significant matching errors. Finally, optimized feature matching improves the accuracy of frame-to-frame registration. Experimental results demonstrate that the proposed method can efficiently and accurately extract corresponding pole-like object features between adjacent frames, enhancing registration stability.