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 are
demonstrated and evaluated by comparing our method with the
state-of-the-art open-source methods, LOAM and LeGo-LOAM,
on KITTI datasets and custom datasets collected by our sensor
system. The experimental results show obvious improvement of
odometry accuracy and mapping consistency without loss of realtime
performance.