A Biologically-Inspired Simultaneous Localization and Mapping System Based on LiDAR Sensor

TL;DR

This paper introduces a biologically inspired SLAM system using LiDAR data, mimicking hippocampal functions to improve indoor robot navigation by leveraging boundary and self-motion cues.

Contribution

It presents a novel hippocampal model-based SLAM system that integrates boundary and place cell mechanisms for improved indoor localization.

Findings

Outperforms camera-based brain-inspired SLAM in simulations and indoor tests.

Achieves competitive accuracy with conventional LiDAR SLAM methods.

Utilizes biologically inspired boundary and place cells for enhanced mapping.

Abstract

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.