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NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation
Zeng TP(曾太平)1,2,3,4; Tang FZ(唐凤珍)3,4; Ji DX(冀大雄)5; Si BL(斯白露)6
Department机器人学研究室
Corresponding AuthorSi, Bailu(bailusi@bnu.edu.cn)
Source PublicationNeural Networks
ISSN0893-6080
2020
Volume126Pages:21-35
Indexed BySCI ; EI
EI Accession number20201108292097
WOS IDWOS:000536450900003
Contribution Rank1
Funding OrganizationNational Key Research and Development Program of China (NO. 2016YFC0801808) ; Natural Science Foundation of China (NO. 51679213) ; CAS Pioneer Hundred Talents Program, China (NO. Y8F1160101) ; State Key Laboratory of Robotics, China (NO. Y7C120E101)
KeywordBayesian Multisensory integration Attractor dynamics Head direction cells Grid cells Monocular SLAM
Abstract

Spatial navigation depends on the combination of multiple sensory cues from idiothetic and allothetic sources. The computational mechanisms of mammalian brains in integrating different sensory modalities under uncertainty for navigation is enlightening for robot navigation. We propose a Bayesian attractor network model to integrate visual and vestibular inputs inspired by the spatial memory systems of mammalian brains. In the model, the pose of the robot is encoded separately by two sub-networks, namely head direction network for angle representation and grid cell network for position representation, using similar neural codes of head direction cells and grid cells observed in mammalian brains. The neural codes in each of the sub-networks are updated in a Bayesian manner by a population of integrator cells for vestibular cue integration, as well as a population of calibration cells for visual cue calibration. The conflict between vestibular cue and visual cue is resolved by the competitive dynamics between the two populations. The model, implemented on a monocular visual simultaneous localization and mapping (SLAM) system, termed NeuroBayesSLAM, successfully builds semi-metric topological maps and self-localizes in outdoor and indoor environments of difference characteristics, achieving comparable performance as previous neurobiologically inspired navigation systems but with much less computation complexity. The proposed multisensory integration method constitutes a concise yet robust and biologically plausible method for robot navigation in large environments. The model provides a viable Bayesian mechanism for multisensory integration that may pertain to other neural subsystems beyond spatial cognition.

Language英语
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS KeywordHEAD-DIRECTION CELLS ; GRID CELLS ; PLACE CELLS ; SIMULTANEOUS LOCALIZATION ; SPATIAL MAP ; REPRESENTATION ; SPACE ; SLAM ; HIPPOCAMPUS ; MODELS
WOS Research AreaComputer Science ; Neurosciences & Neurology
Funding ProjectNational Key Research and Development Program of China[2016YFC0801808] ; Natural Science Foundation of China[51679213] ; CAS Pioneer Hundred Talents Program, China[Y8F1160101] ; State Key Laboratory of Robotics, China[Y7C120E101]
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26447
Collection机器人学研究室
Corresponding AuthorSi BL(斯白露)
Affiliation1.Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
2.Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
5.Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China
6.School of Systems Science, Beijing Normal University, 100875, China
Recommended Citation
GB/T 7714
Zeng TP,Tang FZ,Ji DX,et al. NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation[J]. Neural Networks,2020,126:21-35.
APA Zeng TP,Tang FZ,Ji DX,&Si BL.(2020).NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation.Neural Networks,126,21-35.
MLA Zeng TP,et al."NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation".Neural Networks 126(2020):21-35.
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