Modelling Observation Correlations for Active Exploration and Robust Object Detection
Name
Velez-2012-Modelling Observation Correlations for Active Exploration and Robust Object Detection.pdf
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4.83 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Velez, Javier J.
Huang, Albert S.
Hemann, Garrett A.
Roy, Nicholas
Posner, Ingmar
Date Issued
July 2012
Journal
Journal of Artificial Intelligence Research
Publisher
AI Access Foundation
Citation
J. Velez, G. Hemann, A. S. Huang, I. Posner and N. Roy (2012) Modelling Observation Correlations for Active Exploration and Robust Object Detection. © Copyright 2012 AI Access Foundation, Inc.
Version
Final published version
Abstract
Today, mobile robots are expected to carry out increasingly complex tasks in multifarious, real-world environments. Often, the tasks require a certain semantic understanding of the workspace. Consider, for example, spoken instructions from a human collaborator referring to objects of interest; the robot must be able to accurately detect these objects to correctly understand the instructions. However, existing object detection, while competent, is not perfect. In particular, the performance of detection algorithms is commonly sensitive to the position of the sensor relative to the objects in the scene.
This paper presents an online planning algorithm which learns an explicit model of the spatial dependence of object detection and generates plans which maximize the expected performance of the detection, and by extension the overall plan performance. Crucially, the learned sensor model incorporates spatial correlations between measurements, capturing the fact that successive measurements taken at the same or nearby locations are not independent. We show how this sensor model can be incorporated into an efficient forward search algorithm in the information space of detected objects, allowing the robot to generate motion plans efficiently. We investigate the performance of our approach by addressing the tasks of door and text detection in indoor environments and demonstrate significant improvement in detection performance during task execution over alternative methods in simulated and real robot experiments.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1613/jair.3516
