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.

翻译：同步定位与地图构建（SLAM）是机器人实现自主导航任务的关键技术之一。受啮齿动物海马体启发，本文提出一种基于激光雷达传感器的生物启发式SLAM系统，通过海马体模型构建认知地图并估计室内环境中的机器人位姿。基于模仿边界细胞、位置细胞和方向细胞的生物启发模型，该系统利用激光雷达点云数据的自运动信息和边界细胞提供的边界线索，实现认知地图构建与机器人位姿估计。实验结果表明，引入激光雷达边界细胞后，所提系统在仿真与室内环境中均显著优于基于相机的脑启发方法，且与传统激光雷达SLAM方法性能相当。