Biomimetic SLAM Algorithm Based on Growing Self-Organizing Map

A Biomimetic SLAM Algorithm Based on Growing Self-Organizing Map (GSOM-BSLAM), inspired by spatial cognitive mechanism of mammalian hippocampus, is proposed to resolve uncertainty problems in location identification and lack of real-time performance in simultaneous localization and mapping. The algorithm connects activation characteristics of the place cell and neurons in the output layer of the neural network to construct a topological map of space using a self-organizing growable mapping neural network. It utilizes self-motion-aware information to obtain activation response of the place cell to estimate the robot position information, improving the localization accuracy and real-time performance of the system. Meanwhile, an accurate environmental cognitive map is finally created by incorporating color-depth images for closed-loop detection and error correction for spatial cell path integration. The proposed algorithm is validated using publicly available KITTI and St. Lucia datasets. The experimental results demonstrate that the proposed algorithm outperforms RatSALM by 37.8% and 36.5% in terms of localization accuracy and real-time performance, respectively, indicating good mapping capabilities.