Multisensor Data Fusion

  • Hugh Durrant-WhyteEmail author
  • Thomas C. Henderson
Part of the Springer Handbooks book series (SHB)


Multisensor data fusion is the process of combining observations from a number of different sensors to provide a robust and complete description of an environment or process of interest. Data fusion finds wide application in many areas of robotics such as object recognition, environment mapping, and localization.

This chapter has three parts: methods, architectures, and applications. Most current data fusion methods employ probabilistic descriptions of observations and processes and use Bayes’ rule to combine this information. This chapter surveys the main probabilistic modeling and fusion techniques including grid-based models, Kalman filtering, and sequential Monte Carlo techniques. This chapter also briefly reviews a number of nonprobabilistic data fusion methods. Data fusion systems are often complex combinations of sensor devices, processing, and fusion algorithms. This chapter provides an overview of key principles in data fusion architectures from both a hardware and algorithmic viewpoint. The applications of data fusion are pervasive in robotics and underly the core problem of sensing, estimation, and perception. We highlight two example applications that bring out these features. The first describes a navigation or self-tracking application for an autonomous vehicle. The second describes an application in mapping and environment modeling.

The essential algorithmic tools of data fusion are reasonably well established. However, the development and use of these tools in realistic robotics applications is still developing.


Monte Carlo Kalman Filter Extended Kalman Filter Data Fusion Observation 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.







active sensor network


controller area network


control command interpreter


commercial off-the-shelf


characteristic output vector


decentralized data fusion


distributed field robot architecture


extended Kalman filter


global positioning system


graphical user interface


ground vehicle


human operator


instrumented logical sensor system




joint directors of laboratories


logical sensor system


Monte Carlo


multi expert system for scene interpretation and evaluation


real-time control system


synthetic aperture radar


sensor fusion effect


sampling importance resampling


sequential Monte Carlo


fusing air vehicle


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Australian Centre for Field Robotics (ACFR)University of SydneySydneyAustralia
  2. 2.School of ComputingUniversity of UtahSalt Lake CityUSA

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