부산대학교 도서관

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 article proposes a novel 3-D brain-inspired simultaneous localization and mapping (SLAM) method, called oriented FAST and rotated BRIEF (ORB)-NeuroSLAM, based on the 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 3-D 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 ten KITTI data sets, 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 the state-of-the-art visual SLAM method ORB-SLAM3.