Towards a Predictive Bio-Inspired Navigation Model

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.

The MAPS: Toward a Novel Mobility Assistance System for Visually Impaired People

“…The simulated stereo apparatus, a part of Feedback 2, must be replaced by a “real vision system”, a stereo apparatus embedded in a pair of glasses, and associated with an inertial measurement unit (IMU) (for various obstacles detections and for balance sense simulation). This system will be enhanced with a GPS (or a Galileo) chip for efficient outdoor tracking and reinforcement of our bio-inspired indoor and outdoor mobility model [ 35 ]. Cartographic data necessary for navigation in real environments (indoor and outdoor) will be collected from online services or building blueprints for indoor navigation.…”

“…The physical displacement between adjacent nodes is supposed to be performed straightforward. The path integration algorithm is based on our bio-inspired indoor and outdoor mobility model [ 35 ].…”

Perspective Chapter: Role of the Hippocampal Formation in Navigation from a Simultaneous Location and Mapping Perspective

“…The Grid Cells firing displacement, have a spacing value close to and they are organized an multi-layered form. Each layer represents a different spatial precision of the environment, allowing the increase of certainty, relative to the position of the agent [73].…”

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

“…Bioinspired vision has the characteristics of an efficient neural processing, a low image resolution, and a wide field of view [ 9 ]. Some vision-based navigation validates biological hypotheses and promotes efficient navigation models by mimicking the brain’s navigation system [ 16 ]. The main research directions of the visual-based approaches are optic flow and SLAM.…”

“…It implemented the reward-based learning mechanisms and reaction–diffusion cellular nonlinear networks for perception, albeit only for a simple environment. A novel predictive model of visual navigation approaches based on mammalian navigation with a combination of neurons observed in the brain was presented in [ 16 ] for visually impaired people. It stored the environment representations as place cells and used the grid-cell modules for absolute odometry and an efficient visual system to perform sequential spatial tracking in redundant environments [ 16 ].…”