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Sensing and Estimation

  • Henrik I. ChristensenEmail author
  • Gregory D. Hager
Part of the Springer Handbooks book series (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.

Keywords

Mobile Robot Kalman Filter Graphical Model Sensor Data Gaussian Mixture Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Computer ScienceJohns Hopkins UniversityBaltimoreUSA

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