  Simultaneous Localization And Mapping (SLAM) is a task to estimate the robot
location and to reconstruct the environment based on observation from sensors
such as LIght Detection And Ranging (LiDAR) and camera. It is widely used in
robotic applications such as autonomous driving and drone delivery. Traditional
LiDAR-based SLAM algorithms mainly leverage the geometric features from the
scene context, while the intensity information from LiDAR is ignored. Some
recent deep-learning-based SLAM algorithms consider intensity features and
train the pose estimation network in an end-to-end manner. However, they
require significant data collection effort and their generalizability to
environments other than the trained one remains unclear. In this paper we
introduce intensity features to a SLAM system. And we propose a novel full SLAM
framework that leverages both geometry and intensity features. The proposed
SLAM involves both intensity-based front-end odometry estimation and
intensity-based back-end optimization. Thorough experiments are performed
including both outdoor autonomous driving and indoor warehouse robot
manipulation. The results show that the proposed method outperforms existing
geometric-only LiDAR SLAM methods.
