Unsupervised Place Discovery for Visual Place Classification

In this study, we explore the use of deep convolutional neural networks (DCNNs) in visual place classification for robotic mapping and localization. An open question is how to partition the robot's workspace into places to maximize the performance (e.g., accuracy, precision, recall) of potentia...

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Bibliographic Details
Published in:arXiv.org 2016-12
Main Authors: Fei Xiaoxiao, Tanaka, Kanji, Inamoto Kouya
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Classifiers
Colleges & universities
Mapping
Neural networks
Partitions
Robots
Online Access:Get full text
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Summary:In this study, we explore the use of deep convolutional neural networks (DCNNs) in visual place classification for robotic mapping and localization. An open question is how to partition the robot's workspace into places to maximize the performance (e.g., accuracy, precision, recall) of potential DCNN classifiers. This is a chicken and egg problem: If we had a well-trained DCNN classifier, it is rather easy to partition the robot's workspace into places, but the training of a DCNN classifier requires a set of pre-defined place classes. In this study, we address this problem and present several strategies for unsupervised discovery of place classes ("time cue," "location cue," "time-appearance cue," and "location-appearance cue"). We also evaluate the efficacy of the proposed methods using the publicly available University of Michigan North Campus Long-Term (NCLT) Dataset.
ISSN:2331-8422