Segment-Based SLAM Registration Optimization Algorithm Combining NDT and PL-ICP

With the continuous advancement of LiDAR technology, solid-state LiDAR, with its low cost and unique scanning mode, shows great potential in measurement applications. However, in large-scale environments, the SLAM algorithm LOAM-Livox for solid-state LiDAR often accumulates registration errors, limiting its applicability. To address this, we propose a segment-based SLAM registration optimization algorithm that combines Normal Distributions Transform (NDT) and Point-to-Line Iterative Closest Point (PL-ICP). This algorithm divides the entire data processing into segments, performs SLAM independently on each segment, and registers overlapping areas between adjacent segments to minimize error accumulation. Experiments on both public and self-collected datasets demonstrate that the proposed NDT + PL-ICP optimization algorithm significantly improves the accuracy of mobile mapping with solid-state LiDAR. This approach effectively resolves the error accumulation issue in SLAM, confirming its effectiveness and practicality in real-world applications.