Sensing and estimation

Henrik I. Christensen, Gregory D. Hager

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationSpringer Handbook of Robotics
PublisherSpringer International Publishing
Pages91-112
Number of pages22
ISBN (Electronic)9783319325521
ISBN (Print)9783319325507
DOIs
StatePublished - Jan 1 2016

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Keywords

  • Gaussian mixture model
  • Graphical model
  • Kalman filter
  • Mobile robot
  • Sensor data

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

Cite this

Christensen, H. I., & Hager, G. D. (2016). Sensing and estimation. In Springer Handbook of Robotics (pp. 91-112). Springer International Publishing. https://doi.org/10.1007/978-3-319-32552-1_5