一种基于LiDAR传感器的仿生同时定位与建图系统
A Biologically-Inspired Simultaneous Localization and Mapping System Based on LiDAR Sensor
AI总结：
受啮齿类动物海马体启发，提出基于LiDAR与边界、位置等细胞模型的仿生SLAM系统，在室内环境构建认知地图并估计位姿，性能优于相机方法且媲美传统LiDAR SLAM。
摘要：
同时定位与建图（SLAM）是机器人执行自主导航任务所需的核心技术与功能之一。受啮齿类动物海马体启发，本文提出了一种基于LiDAR传感器的仿生SLAM系统，该系统利用海马体模型在室内环境中构建认知地图并估计机器人位姿。基于模拟边界细胞、位置细胞和头方向细胞的仿生模型，该使用LiDAR点云数据的SLAM系统能够利用来自LiDAR里程计的自运动线索和来自LiDAR边界细胞的边界线索来构建认知地图并估计机器人位姿。实验结果表明，借助LiDAR边界细胞，所提出的SLAM系统在仿真和室内环境中均大幅优于基于相机的脑启发方法，并且与传统的基于LiDAR的SLAM方法具有竞争力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
