ArticleFrontiers in neurorobotics2017
Cognitive Mapping Based on Conjunctive Representations of Space and Movement.
Article in Frontiers in neurorobotics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- NeoSLAM: Long-Term SLAM Using Computational Models of the Brain.Sensors (Basel, Switzerland) · 2024Article
- Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review.Biomimetics (Basel, Switzerland) · 2023Review
- Exploiting semantic information in a spiking neural SLAM system.Frontiers in neuroscience · 2023Article
- Entorhinal-hippocampal interactions lead to globally coherent representations of space.Current research in neurobiology · 2022Review
- Bayesian models of human navigation behaviour in an augmented reality audiomaze.The European journal of neuroscience · 2021Article
- A brain-inspired compact cognitive mapping system.Cognitive neurodynamics · 2021Article
- Multi-Scale Extension in an Entorhinal-Hippocampal Model for Cognitive Map Building.Frontiers in neurorobotics · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
It is a challenge to build robust simultaneous localization and mapping (SLAM) system in dynamical large-scale environments. Inspired by recent findings in the entorhinal-hippocampal neuronal circuits, we propose a cognitive mapping model that includes continuous attractor networks of head-direction cells and conjunctive grid cells to integrate velocity information by conjunctive encodings of space and movement. Visual inputs from the local view cells in the model provide feedback cues to correct drifting errors of the attractors caused by the noisy velocity inputs. We demonstrate the mapping performance of the proposed cognitive mapping model on an open-source dataset of 66 km car journey in a 3 km × 1.6 km urban area. Experimental results show that the proposed model is robust in building a coherent semi-metric topological map of the entire urban area using a monocular camera, even though the image inputs contain various changes caused by different light conditions and terrains. The results in this study could inspire both neuroscience and robotic research to better understand the neural computational mechanisms of spatial cognition and to build robust robotic navigation systems in large-scale environments.
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Registered trials
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