ORB-NeuroSLAM: A Brain-Inspired 3-D SLAM System Based on ORB Features

摘要

Intelligent navigation is a fundamental technology that enables unmanned systems to achieve autonomy in the intelligent era. However, existing navigation schemes suffer from high computational complexity and power consumption, as well as low robustness in complex or unknown environments. To address these challenges, this paper proposes a novel 3D brain-inspired simultaneous localization and mapping (SLAM) method, called ORB-NeuroSLAM, based on the oriented FAST and rotated BRIEF (ORB) features. The proposed method takes inspiration from the robust and low-power navigation capabilities of humans and animals. The ORB-NeuroSLAM leverages the ORB features of camera images to compute robot self-motion and visual cues. Then, continuous attractor neural networks (CANNs) model multilayered head direction cells and three-dimensional grid cells that exist in animal brains. These cells are utilized jointly to represent the robot poses. Efficient and robust methods for loop closure detection and experience map construction were also developed. The proposed method was verified on 10 KITTI datasets, and experimental results demonstrate that it outperforms state-of-the-art brain-inspired SLAM methods in terms of accuracy and efficiency. Additionally, it is comparable to state-of-the-art visual SLAM method ORB-SLAM3.