  Simultaneous localization and mapping (SLAM) is one of the essential
techniques and functionalities used by robots to perform autonomous navigation
tasks. Inspired by the rodent hippocampus, this paper presents a biologically
inspired SLAM system based on a LiDAR sensor using a hippocampal model to build
a cognitive map and estimate the robot pose in indoor environments. Based on
the biologically inspired models mimicking boundary cells, place cells, and
head direction cells, the SLAM system using LiDAR point cloud data is capable
of leveraging the self-motion cues from the LiDAR odometry and the boundary
cues from the LiDAR boundary cells to build a cognitive map and estimate the
robot pose. Experiment results show that with the LiDAR boundary cells the
proposed SLAM system greatly outperforms the camera-based brain-inspired method
in both simulation and indoor environments, and is competitive with the
conventional LiDAR-based SLAM methods.
