Sensing and Estimation

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


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.


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.

computer-aided design


charge-coupled detector


conditional random field




extended Kalman filter


expectation maximization


generalized principal component analysis


global positioning system


iterative closest point


iterated extended Kalman filter


inertial measurement unit




iteratively reweighted least square


least median of squares


maximum a posteriori


maximum likelihood estimate


minimum mean-square error


National Television System Committee




random sample consensus


radio frequency identification


radio frequency






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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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