IMIS

[ report an error in this record ]basket (0): add | show Print this page
Sensing and estimation
Christensen, H.I.; Hager, G.D. (2016). Sensing and estimation, in: Siciliano, B. et al. Springer handbook of robotics. pp. 91-112. https://dx.doi.org/10.1007/978-3-319-32552-1_5
In: Siciliano, B.; Khatib, O. (Ed.) (2016). Springer handbook of robotics. Second edition. Springer Verlag: Berlin. ISBN 978-3-319-32550-7; e-ISBN 978-3-319-32552-1. LXXVI, 2227 pp. https://dx.doi.org/10.1007/978-3-319-32552-1, more
Authors  Top 
  • Christensen, H.I.
  • Hager, G.D.
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.
All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors