Abstract
This paper presents a novel bio-inspired predictive model of visual navigation inspired by mammalian navigation. This model takes inspiration from specific types of neurons observed in the brain, namely place cells, grid cells and head direction cells. In the proposed model, place cells are structures that store and connect local representations of the explored environment, grid and head direction cells make predictions based on these representations to define the position of the agent in a place cell’s reference frame. This specific use of navigation cells has three advantages: First, the environment representations are stored by place cells and require only a few spatialized descriptors or elements, making this model suitable for the integration of large-scale environments (indoor and outdoor). Second, the grid cell modules act as an efficient visual and absolute odometry system. Finally, the model provides sequential spatial tracking that can integrate and track an agent in redundant environments or environments with very few or no distinctive cues, while being very robust to environmental changes. This paper focuses on the architecture formalization and the main elements and properties of this model. The model has been successfully validated on basic functions: mapping, guidance, homing, and finding shortcuts. The precision of the estimated position of the agent and the robustness to environmental changes during navigation were shown to be satisfactory. The proposed predictive model is intended to be used on autonomous platforms, but also to assist visually impaired people in their mobility.
Highlights
Navigating in an environment, whether indoor or outdoor, is a fundamental task for autonomous robotic systems and a vital task for many species
This paper proposes new answers to the above questions through its novel predictive approach to indoor and outdoor navigation based on collaboration between place cells, grid cells and head direction cells [5]
Adding New Place Cells to the Navigation Graph Grid cells are used to determine when a new place cell has to be added: as the current place cell defines the center of a grid cell module, it defines border grid cells. These cells are the grid cells with the greatest distances to the module center (Figure 7). Reaching one of these border grid cells indicates that the agent is moving out of the module’s area of coverage centered on the current place cell
Summary
Navigating in an environment, whether indoor or outdoor, is a fundamental task for autonomous robotic systems and a vital task for many species. A navigation model is continually updated based on location recognition and odometry data, the robot being simultaneously localized within the map Each node of this graph represents a particular waypoint, while edges represent accessibility between waypoints. High precision and minimal computational costs are the main requirements for such navigation These approaches usually perform better in environments of limited size [3]. Instead of creating a global map during the exploration of the environment, these approaches generate a graph model, where nodes are local representations (i.e., at a limited distance) of the observed environment in that node This class of approaches is inspired by place cells [4], a spatially correlated neuron found in mammalian brains.
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