A LiDAR SLAM With PCA-Based Feature Extraction and Two-Stage Matching

Abstract

Simultaneous localization and mapping (SLAM) has been studied for decades in the field of robotics, in which light detection and ranging (LiDAR) is widely used in various application areas benefiting from its accessibility of direct, accurate, and reliable 3-D measurements. However, the performance of LiDAR SLAM may be degraded when running in degenerate scenario, which makes it still a challenging problem to realize real-time, robust, and accurate state estimation in complex environments. In this article, we propose a keyframe-based 3-D LiDAR SLAM using an accurate principal component analysis (PCA)-based feature extraction method and an efficient two-stage matching strategy, toward a more robust, accurate, and globally consistent estimation performance. The effectiveness and performance