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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Active Classification: Theory and Application to Underwater Inspection

verfasst von : Geoffrey A. Hollinger, Urbashi Mitra, Gaurav S. Sukhatme

Erschienen in: Robotics Research

Verlag: Springer International Publishing

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Abstract

We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We formally analyze the benefit of acting adaptively as new information becomes available. The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs. We validate our approach in two ways, both related to underwater inspection: 3D polyhedra recognition in synthetic depth maps and ship hull inspection with imaging sonar. These tasks encompass both the planning and recognition aspects of the active classification problem. The results demonstrate that actively planning for informative views can reduce the number of necessary views by up to 80 % when compared to passive methods.

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Vorheriges Kapitel Probabilistic Collision Detection Between Noisy Point Clouds Using Robust Classification
Nächstes Kapitel The Importance of Structure
Fußnoten
1
There are a number of additional active vision works relevant to the present paper. We direct the interested reader to Roy et al. [17] for a survey.
 
2
We formulate the problem for the case of discrete locations. If continuous locations are available, an interpolation function can be used to estimate the informativeness of a location based on the discrete training data (see Sect. 6).
 
3
Note that the related problem of minimizing expected loss subject to a hard constraint on budget is also relevant. While similar examples show that there is a benefit to acting adaptively in this case, we defer detailed analysis to future work.
 
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Metadaten
Titel
Active Classification: Theory and Application to Underwater Inspection
verfasst von
Geoffrey A. Hollinger
Urbashi Mitra
Gaurav S. Sukhatme
Copyright-Jahr
2017
Buch
Robotics Research

Print ISBN: 978-3-319-29362-2

Electronic ISBN: 978-3-319-29363-9

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-29363-9_6

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