Applying uncertainty reasoning to model based object recognition

1993

SPIE Proceedings

This paper presents an applied practical comparison of Bayesian and Dernpster-Sliafer techniques useful for managing uncertainty in sensing. Three formulations of the same example are presented: a Bayesian, a naive Dempster-Shafer, and a Dempster-Shafer approach using a refined frame of discernment. Both the Bayesian and Dempster-Shafer (with a refined frame of discernment) yield similar results; however, information content and representations are different between the two methods. Bayesian theory requires a more explicit formulation of conditioning and the prior probabilities of events. Dempster-Shafer theory embeds conditioning information into its belief function and does not rely on prior knowledge, making it appropriate for situations where it is difficult to either collect or posit such probabijities, or isolate their contribution.

“…Refinement refers to the decomposition of focal elements into logical "sub-focal elements." This refinement process has been successfully demonstrated by Hutchinson [9].…”

Section: Comments On the Naive Dempster-shafer Approachmentioning

confidence: 80%

“…. ,EIA] = PiiE"14] (7) And, (9) In Section 3 we will show how these results apply to an example of uncertainty management based on Bayesian analysis.…”

Section: Pr[aie)mentioning

confidence: 89%

<title>Comparison of Bayesian and Dempster-Shafer theory for sensing: a practitioner's approach</title>

Hoffman

1993

SPIE Proceedings

“…Subsequent references to "probabilities" will be reserved for true statistical probabilities (i.e., Bayesian) while "belief function" will be used for the DS probabilities. DS theory has been considered for a variety of perceptual activities including sensor fusion [7], [8], [14], scene interpretation [25], object/target recognition [19], [20], and object verification [44]. The systems of [19], [20], [44] are particularly relevant because they view DS in terms of sensor characteristics.…”

Section: Evidential Methods For Sensor Fusion At the Symbol Levelmentioning

confidence: 99%

“…Keller and Hobson [20] in 1989 implemented interpretation rules for translating the belief in one FOD to another, again with no explicit theoretical justification. At about the same time, Hutchinson, Cromwell, and Kak [19] noted that for their application some FOD's could be considered refinements of another and transferred belief according to [47], permitting an implementation that would remain fully consistent with DS theory. Liu, Hughes, and McTear formalized the representation of heuristic knowledge as an evidential mapping between frames of discernment in [27].…”

Section: B Evidential Mappingsmentioning

confidence: 99%

Dempster-Shafer theory for sensor fusion in autonomous mobile robots

IEEE Trans. Robot. Automat.

1998

184

This article discusses Dempster-Shafer (DS) theory in terms of its utility for sensor fusion for autonomous mobile robots. It exploits two little used components of DS theory: the weight of conflict metric and the enlargement of the frame of discernment. The weight of conflict is used to measure the amount of consensus between different sensors. A lack of consensus leads the robot to either compensate within certain limits or investigate the problem further, adding robustness to the robot's operation. Enlarging the frame of discernment allows a modular decomposition of evidence. This decomposition offers the advantages of perceptual abstraction, and permits expert knowledge about the domain to be embedded in the frames of discernment, simplifying the construction and maintenance of the knowledge base. Six experiments using this Dempster-Shafer framework are presented. Data from four types of sensor data were collected by a mobile robot and fused with the Sensor Fusion Effects (SFX) architecture.

“…DS theory has been considered for perceptual activities such as sensor fusion [6,8,14], scene interpretation [22], object/target recognition [17,19], building environmental maps [33], and object verification [27]. The systems of [17,19,27] are particularly relevant to our research because they view DS in terms of sensor characteristics.…”

Section: Previous Work In Applying Ds Theorymentioning

confidence: 99%

<title>Application of Dempster-Shafer theory to a novel control scheme for sensor fusion</title>

1991

SPIE Proceedings

The combination of imperfect evidence contributed by different sensors is a basic problem for sensor fusion in autonomous mobile robots. Current implementations of sensor fusion systems are restricted to fusing only certain classes of evidence because of the lack of a general framework for the combination of evidence.Our approach to this problem is to first develop a model of the sensor fusion without committing to a particular theory of evidence, then formulate a combination of evidence framework based on the requirements of the model. Our previous work has proposed such a model. This paper discusses the evidential demands of the model and one possible implementation using Dempster-Shafer theory. Three drawbacks of DS theory (computational intractability, weak assumptions of statistical independence, and counterintuitive averaging of strongly biased evidence) are eliminated by applying DS theory within the constraints of the model. An example based on simulated sensor data illustrates this application of Dempster-Shafer theory.