Dempster-Shafer theory for sensor fusion in autonomous mobile robots

Abstract

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.

Conflict evaluation method for grid maps using sonar sensors

“…As mentioned in the introduction to this paper, in [9], [10], [11], it has been revealed that the inconsistent cell decreases the quality of the map. To probe further, we try to classify an inconsistent cell into one of two groups, the uncertain cell ( Fig.…”

“…Because the basic motivation of all these methods is the uncertainty of sonar readings and the improvement of the grid map quality, several studies [9], [10], [11] have reported that the inconsistent cell is the main source of poor map quality. The inconsistent cell is the grid that contains contradictory information; e.g., a sonar reading indicates the cell as occupied but another indicates it is empty.…”

Reliability measure assignment to sonar for robust target differentiation

“…[37]. Evidential reasoning theory has also been applied to robotics [35,[38][39][40] and to model-based failure diagnosis [41]. A comparison of Bayesian and Dempster-Shafer multi-sensor fusion for target identiÿ-cation is provided in Ref.…”

Event Detection in Wireless Sensor Networks – Can Fuzzy Values Be Accurate?

“…Bayesian classifiers and Hidden Markov Models have been extensively used in activity recognition [8,9] and decision fusion [10,11]. Dempster-Shafer evidence theory has been applied to intrusion detection [12], sensor fusion [13,14], and assisted living applications [15]. Probabilistic context free grammars have been used to solve problems such as inferring behaviors [16] as well as movement and activity monitoring [17,18].…”