Spatio-Temporal Sequence Learning of Visual Place Cells for Robotic Navigation presented by Nguyen Vu Anh date: 20 th July, 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 IJCNN, WCCI, Barcelona, Spain, 2010
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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 - PowerPoint PPT Presentation
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Spatio-Temporal Sequence Learning of Visual Place Cells for Robotic Navigation
presented by Nguyen Vu Anh date: 20th July, 2010
Nguyen Vu Anh, Alex Leng-Phuan Tay, Wooi-Boon Goh
School of Computer EngineeringNanyang Technological University
Singapore
Janusz A. StarzykSchool of Electrical Engineering
Ohio University Athens, USA
IJCNN, WCCI, Barcelona, Spain, 2010
Outline
• Introduction• HMAX Feature Building and Extraction• Spatio-Temporal Learning and Recognition• Empirical Results• Conclusion and future directions
Introduction
• 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
Introduction
• System Architecture
Classifier
Sequence
Storage
Symbol
Quantization
Feature
Building
and
Extraction
Introduction
• Existing Works:
– Autonomous navigation (SLAM): Mapping, Localization and Path Planning • Topological vs metric representation• Human employs mainly topological representation of environment [O’Keefe
– Quantization of input using KFLANN with vigilance ρ
See: Tay, Zurada,Wong and Xu,
TNN, 2007
Spatio-Temporal Learning Architecture
• STM Structure:
See: Tay, Zurada,Wong and Xu, TNN, 2007
Spatio-Temporal Learning Architecture
• 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 Architecture
• LTM Cell Structure:
Dual Neurons –
STM
Primary Neurons –
Primary Excitation
Spatio-Temporal Learning Architecture
• Storage– One-shot learning
• Recognition
Input feature vector
Primary Excitation
Computation
Dual Neurons Update – Evidence Accumulation
Output Matching Score from the last DN
Empirical Results
• 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 Results
• 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 Results
Table: LTM listing with training set S
Empirical Results
• Accuracy without threshold
• Accuracy with threshold ө=0.4
• Robust testing: missing elements
Empirical Results
Figure: LTM cells’ activation during recall stage
Empirical Results
• Intersection case:
Conclusion
• 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
• 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