A Fast and Accurate Planar-Feature-Based Global Scan Registration Method

Abstract

Scan registration is the basis of LiDAR-based mapping and localization. When a good initial guess is available, classical local registration algorithms such as iterative closet point (ICP) and normal distributions transform (NDT) can be utilized. However, scan registration without odometer or inertial measurement unit (IMU) is still an open problem. After reviewing the existing methods, we summarize a modular planar-feature-based registration framework. Based on the idea of modular design, we present a novel global scan registration method based on planar features. A modified renormalization-based algorithm considering the sensor noise model is derived and used for accurate plane fitting and covariance computing. An efficient adaptive plane segmentation algorithm is employed to extract planar patches which is based on voxel growing. A novel projected-image-based method is utilized for fast plane attribute calculation. The attributes of individual planes and relation constrains between the planes are employed to robustly build plane correspondences. The plane-plane singular value decomposition (SVD) method is used to estimate the transform, and normalization of the point coordinates is adopted to further improve accuracy. A consensus metric is introduced to determine plane correspondences and transform solution updating, and to select the optimal solution using the distance from the centroid of one plane to its conjugate plane. Extensive comparative experiments were performed on four datasets covering different structured scenarios to evaluate the performance of the proposed registration method. The experimental results demonstrate that the proposed method achieves a better balance between accuracy and efficiency, and has more stable and robust performance across different scenarios compared to all tested state-of-the-art algorithms.