Spatio temporal sequence learning of visual place cells for robotic navigation
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Spatio-Temporal Sequence Learning of Visual Place Cells for Robotic Navigation. IJCNN, WCCI, Barcelona, Spain, 2010. Nguyen Vu Anh, Alex Leng-Phuan Tay, Wooi-Boon Goh School of Computer Engineering Nanyang Technological University Singapore. Janusz A. Starzyk

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Spatio temporal sequence learning of visual place cells for robotic navigation
Spatio-Temporal Sequence Learning of Visual Place Cells for Robotic Navigation

IJCNN, WCCI, Barcelona, Spain, 2010

Nguyen Vu Anh, Alex Leng-Phuan Tay,

Wooi-Boon Goh

School of Computer Engineering

Nanyang Technological University

Singapore

Janusz A. Starzyk

School of Electrical Engineering

Ohio University

Athens, USA

presented by Nguyen Vu Anh

date: 20th July, 2010


Outline
Outline Robotic Navigation

  • Introduction

  • HMAX Feature Building and Extraction

  • Spatio-Temporal Learning and Recognition

  • Empirical Results

  • Conclusion and future directions


Introduction
Introduction Robotic Navigation

  • Robotic navigation: Localization and Mapping.

    • Topological map & Place cells

    • Scope: Topological Visual Localization

  • Challenges:

    • High dimension and uncertainty of visual features

    • Perceptual aliasing

    • Complex probabilistic frameworks e.g. HMM

  • Approach:

    • Structural organization of human memory architecture.

    • Short-Term Memory (STM) and Long-Term Memory(LTM) Interaction


Introduction1
Introduction Robotic Navigation

Classifier

  • System Architecture

SequenceStorage

Symbol Quantization

Feature Building and Extraction


Introduction2
Introduction Robotic Navigation

  • Existing Works:

    • Autonomous navigation (SLAM): Mapping, Localization and Path Planning

      • Topological vs metric representation

      • Human employs mainly topological representation of environment[O’Keefe (1976), Redish(1999), Eichenbaum (1999), etc]

    • Visual Place-cell model: [Torralba (2001) ; Renninger&Malik (2004) ; Siagian&Itti (2007)]

      • Hierarchical feature building and extraction (HMAX Model) [Serre et al (2007)]

    • Spatio-Temporal sequence learning: [Wang&Arbib (1990) (1993), Wang&Yowono (1995)]

      • Our previous works: [Starzyk&He, (2007);Starzyk&He (2009);Tay et al (2007);Nguyen&Tay (2009)]


Hmax feature building and extraction
HMAX Feature Building and Extraction Robotic Navigation

  • Interleaving simple (S) and complex (C) layers with increasing spatial invariance (Retina - LGN – V1 – V2,V4)

  • 2 Stages:

    • Feature Construction

    • Feature Extraction

  • Feature Significance:


Hmax feature building and extraction1
HMAX Feature Building and Extraction Robotic Navigation

Dot-Product Matching

Spatial Invariance Processing

Prototypes

Ref: Riesenhuber & Poggio (1999),Serre et al (2007)


Spatio temporal learning architecture
Spatio-Temporal Learning Architecture Robotic Navigation

  • STM Structure:

    • Quantization of input using KFLANN with vigilance ρ

See: Tay, Zurada,Wong and Xu, TNN, 2007


Spatio temporal learning architecture1
Spatio-Temporal Learning Architecture Robotic Navigation

  • STM Structure:

See: Tay, Zurada,Wong and Xu, TNN, 2007


Spatio temporal learning architecture2
Spatio-Temporal Learning Architecture Robotic Navigation

  • LTM Cell Structure:

    • Each LTM is learnt by one-shot mechanism.

    • Each long training sequence is segmented into N overlapping subsequences of the same length M.

    • Each subsequence is dedicated permanently to an LTM cell.


Spatio temporal learning architecture3
Spatio-Temporal Learning Architecture Robotic Navigation

  • LTM Cell Structure:

Dual Neurons – STM

Primary Neurons – Primary Excitation


Spatio temporal learning architecture4
Spatio-Temporal Learning Architecture Robotic Navigation

  • Storage

    • One-shot learning

  • Recognition

Input feature vector

Primary ExcitationComputation

Dual Neurons Update – Evidence Accumulation

Output Matching Score from the last DN


Empirical results
Empirical Results Robotic Navigation

  • ICLEF Competition 2010 Dataset

    • 9 classes of places

    • 2 sets of images with the same trajectory (Set S and SetC) (~4000 images each set)

C

K

L

O


Empirical results1
Empirical Results Robotic Navigation

  • Task

    • 1 sequence (Set S) as training set and 1 sequence as testing set (Set R).

  • Features:

    • 10% of the training sequence

  • Training

    • ρ=0.7.

    • Segmentation into consecutive subsequences of equal length (100) with overlapping portion (>50%).

    • Each subsequence is stored as a LTM cell.

    • The label of each LTM cell is the majority label of individual components.

  • Testing

    • The label is assigned as the label of the maximally activated LTM cell.

    • If the activation of the maximal activated LTM cell is below ө, the system refuses to assign the label.


Empirical results2
Empirical Results Robotic Navigation

Table: LTM listing with training set S


Empirical results3
Empirical Results Robotic Navigation

  • Accuracy without threshold

  • Accuracy with threshold ө=0.4

  • Robust testing: missing elements


Empirical results4
Empirical Results Robotic Navigation

Figure: LTM cells’ activation during recall stage


Empirical results5
Empirical Results Robotic Navigation

  • Intersection case:


Conclusion
Conclusion Robotic Navigation

  • A hierarchical spatio-temporal learning architecture

    • HMAX hierarchical feature construction and extraction

    • STM clustering by KFLANN

    • Sequence storage and retrieval by LTM cells.

  • Application in appearance-based topological localization


Future directions
Future Directions Robotic Navigation

  • Automatic tolerance estimation

    • E.g. Signal-to-noise ratio figure of features [Liu&Starzyk 2008]

  • Hierarchical episodic memory which characterizes the interaction between STM and LTM

    • Other embodied intelligence components

    • Goal creation system [Starzyk 2008]

  • Application in other domains:

    • Human Action Recognition


Thank you! Robotic Navigation


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