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 colordepth 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.
In response to problems concerning the low autonomous localization accuracy of mobile robots in unknown environments and large cumulative errors due to long time running, a spatial location representation method incorporating boundary information (SLRB) is proposed, inspired by the mammalian spatial cognitive mechanism. In modeling the firing characteristics of boundary cells to environmental boundary information, we construct vector relationships between the mobile robot and environmental boundaries with direction-aware information and distance-aware information. The self-motion information (direction and velocity) is used as the input to the lateral anti-Hebbian network (LAHN) to generate grid cells. In addition, the boundary cell response values are used to update the grid cell distribution law and to suppress the error response of the place cells, thus reducing the localization error of the mobile robot. Meanwhile, when the mobile robot reaches the boundary cell excitation zone, the activated boundary cells are used to correct the accumulated errors that occur due to long running times, which thus improves the localization accuracy of the system. The main contributions of this paper are as follows: 1. We propose a novel method for constructing boundary cell models. 2. An approach is presented that maps the response values of boundary cells to the input layer of LAHN (Location-Adaptive Hierarchical Network), where grid cells are generated through LAHN learning rules, and the distribution pattern of grid cells is adjusted using the response values of boundary cells. 3. We correct the cumulative error caused by long-term operation of place cells through the activation of boundary cells, ensuring that only one place cell responds to the current location at each individual moment, thereby improving the positioning accuracy of the system.
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