Task-directed multi-sensor fusion

Greg Hager, Max Mintz

Research output: Contribution to conferencePaperpeer-review


The authors consider the problem of task-directed information gathering. They first develop a decision-theoretic model of task-directed sensing. In this framework, sensors are modeled as noise-contaminated, uncertain measurement systems. A a sensor task consists of a function describing the type of information required by the task, a utility function describing sensitivity to error, and a cost function describing time or resource constraints on the system. From this description, the authors develop a computational method approximating a standard Bayesian decision-making model. This algorithm, which relies on a finite-element computation, is applicable to a wide variety of sensor fusion problems. The authors describe its derivation, analyze its error properties, and indicate how it can be made robust to errors in the description of sensors and discrepancies between geometric models and sensed obects. They also present the result of applying this fusion technique to several different information gathering tasks in simulated situations and in a distributed sensing system.

Original languageEnglish (US)
Number of pages6
StatePublished - Dec 1 1989
Externally publishedYes
EventIEEE International Conference on Robotics and Automation - 1989 - Scottsdale, AZ, USA
Duration: May 14 1989May 19 1989


OtherIEEE International Conference on Robotics and Automation - 1989
CityScottsdale, AZ, USA

ASJC Scopus subject areas

  • Engineering(all)


Dive into the research topics of 'Task-directed multi-sensor fusion'. Together they form a unique fingerprint.

Cite this