文献类型：期刊文献

英文题名：A neuro-inspired visual SLAM approach using AKAZE feature extraction in complex and dynamic environments

作者：Li, Ruibang[1];Wang, Yihong[1,2];Xu, Xuying[1,2];Li, Fangfei[1,2];Tang, Fengzhen[3];Pan, Xiaochuan[1,2]

机构：[1]East China Univ Sci & Technol, Inst Cognit Neurodynam, Sch Math, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Ctr Intelligent Comp, Sch Math, Shanghai 200237, Peoples R China;[3]Chinese Acad Sci, Shenyang Inst Automat, Shenyang 110016, Liaoning, Peoples R China

年份：2025

卷号：20

期号：1

外文期刊名：COGNITIVE NEURODYNAMICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001631464000003)】；

基金：This study is supported by the National Natural Science Foundation of China (Grant Nos. 12172132, 12272136 and 12472054) and Science and Technology Commission of Shanghai Municipality (No. 24JS2810400).

语种：英文

外文关键词：Brain-inspired navigation; Simultaneous localization and mapping (SLAM); RatSLAM; Loop closure detection; AKAZE features

摘要：Place cells and head direction cells in the rodent brain encode spatial position and orientation, forming the neural basis for navigation and cognitive map construction. Inspired by these mechanisms, RatSLAM simulates their roles to achieve biologically inspired visual SLAM. However, traditional RatSLAM struggles with robust feature extraction in visually complex or dynamic environments, where features may be unstable or non-distinct. To address this, we integrate the AKAZE algorithm into the RatSLAM framework. AKAZE combines accelerated techniques with nonlinear diffusion filtering to construct a multi-scale nonlinear scale space, enabling efficient extraction of robust, scale-invariant features across spatial scales. These features are incorporated into RatSLAM's local view module to improve loop closure detection and mitigate odometry drift. Traditional evaluation approaches rely on real-time pose trajectories and cannot evaluate the trajectories based on the fully optimized experience maps, leading to inaccurate mapping performance assessments. Thus, we further propose a novel Ray-Based Map Metric Error Evaluation Method, which can directly compare the final experience maps generated by RatSLAM. Experiments on the KITTI dataset demonstrate that, compared with both ORB-RatSLAM and the ORB-SLAM3, the proposed AKAZE-RatSLAM achieves higher loop closure recall and mapping accuracy while maintaining a lightweight computational profile. In particular, CPU and memory measurements show that AKAZE-RatSLAM requires significantly less computational resources than ORB-SLAM3, confirming its suitability for real-time deployment on resource-limited robotic platforms. Furthermore, neuro-inspired analyses reveal that the pose cell network exhibits spatially localized and direction-selective firing patterns analogous to hippocampal place cells and head direction cells in rodents. Specifically, cells along the same row encode adjacent spatial regions, forming continuous place-field-like activations, whereas cells in the same column show distinct preferred orientations, indicating directional tuning. These biological characteristics confirm that the proposed AKAZE-RatSLAM not only enhances mapping performance and efficiency but also preserves the neurobiological plausibility of spatial representation, advancing the development of brain-inspired visual SLAM systems.