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Sensing and Estimation

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Springer Handbook of Robotics

Part of the book series: Springer Handbooks ((SHB))

Abstract

Sensing and estimation are essential aspects of the design of any robotic system. At a very basic level, the state of the robot itself must be estimated for feedback control. At a higher level, perception, which is defined here to be task-oriented interpretation of sensor data, allows the integration of sensor information across space and time to facilitate planning.

This chapter provides a brief overview of common sensing methods and estimation techniques that have found broad applicability in robotics. The presentation is structured according to a process model that includes sensing, feature extraction, data association, parameter estimation, and model integration. Several common sensing modalities are introduced and characterized. Common methods for estimation in linear and nonlinear systems are discussed, including statistical estimation, the Kalman filter, and sample-based methods. Strategies for robust estimation are also briefly described. Finally, several common representations for estimation are introduced.

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Abbreviations

CAD:

computer-aided design

CCD:

charge-coupled detector

CRF:

conditional random field

EC:

exteroception

EKF:

extended Kalman filter

EM:

expectation maximization

GPCA:

generalized principal component analysis

GPS:

global positioning system

ICP:

iterative closest point

IEKF:

iterated extended Kalman filter

IMU:

inertial measurement unit

IR:

infrared

IRLS:

iteratively reweighted least square

LMedS:

least median of squares

MAP:

maximum a posteriori

MLE:

maximum likelihood estimate

MMSE:

minimum mean-square error

NTSC:

National Television System Committee

PC:

proprioception

RANSAC:

random sample consensus

RFID:

radio frequency identification

RF:

radio frequency

RGB-D:

red–green–blue–depth

UV:

ultraviolet

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Authors and Affiliations

  1. Robotics and Intelligent Machines, Georgia Institute of Technology, 801 Atlantic Drive NW, GA 30332-0280, Atlanta, USA

    Henrik I. Christensen

  2. Department of Computer Science, Johns Hopkins University, 3400 North Charles Street, MD 21218, Baltimore, USA

    Gregory D. Hager

Authors
  1. Henrik I. Christensen
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  2. Gregory D. Hager
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Correspondence to Henrik I. Christensen .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy

    Bruno Siciliano

  2. Department of Computer Sciences, Artificial Intelligence Laboratory, Stanford University, 450 Serra Mall, CA 94305, Stanford, USA

    Oussama Khatib

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Christensen, H.I., Hager, G.D. (2016). Sensing and Estimation. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-32552-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-32552-1_5

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