Published on Wed Jul 04 2012

Use of Dempster-Shafer Conflict Metric to Detect Interpretation Inconsistency

Jennifer Carlson, Robin R. Murphy

A model of the world built from sensor data may be incorrect even if the sensors are functioning correctly. Possible causes include the use ofappropriate sensors (e.g. a laser looking through glass walls) or the internal representation does not match the world. This paper considers the problem of determining when something is wrong using only the sensor

0
0
0
Abstract

A model of the world built from sensor data may be incorrect even if the sensors are functioning correctly. Possible causes include the use of inappropriate sensors (e.g. a laser looking through glass walls), sensor inaccuracies accumulate (e.g. localization errors), the a priori models are wrong, or the internal representation does not match the world (e.g. a static occupancy grid used with dynamically moving objects). We are interested in the case where the constructed model of the world is flawed, but there is no access to the ground truth that would allow the system to see the discrepancy, such as a robot entering an unknown environment. This paper considers the problem of determining when something is wrong using only the sensor data used to construct the world model. It proposes 11 interpretation inconsistency indicators based on the Dempster-Shafer conflict metric, Con, and evaluates these indicators according to three criteria: ability to distinguish true inconsistency from sensor noise (classification), estimate the magnitude of discrepancies (estimation), and determine the source(s) (if any) of sensing problems in the environment (isolation). The evaluation is conducted using data from a mobile robot with sonar and laser range sensors navigating indoor environments under controlled conditions. The evaluation shows that the Gambino indicator performed best in terms of estimation (at best 0.77 correlation), isolation, and classification of the sensing situation as degraded (7% false negative rate) or normal (0% false positive rate).

Mon Mar 12 2018
Artificial Intelligence
Multi-Sensor Conflict Measurement and Information Fusion
In sensing applications where multiple sensors observe the same scene, fusing outputs can provide improved results. However, if some of the sensors are providing lower quality outputs, the fused results can be degraded. This work is a preliminary step towards a robust conflict and sensor fusion framework. The proposed methods can be used to assess a measure of multi-sensor conflict.
0
0
0
Mon Mar 08 2021
Computer Vision
Advances in Inference and Representation for Simultaneous Localization and Mapping
0
0
0
Mon Jun 20 2011
Artificial Intelligence
Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications
ontologies present a growing interest in Data Fusion applications. Different probabilistic, fuzzy and fuzzy approaches already exist to fill this gap. However none of the tools meets exactly our purposes. We constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology.
0
0
0
Wed Mar 27 2013
Artificial Intelligence
Probabilistic Conflict Resolution in Hierarchical Hypothesis Spaces
Artificial intelligence applications such as industrial robotics, military surveillance, and hazardous environment clean-up, require situation understanding based on partial, uncertain, and ambiguous or erroneous evidence.
0
0
0
Wed Mar 27 2013
Artificial Intelligence
Bayesian Inference in Model-Based Machine Vision
Bayesian inference provides a framework for accruing probabilities to rank hypothesized hypotheses. We pursue a thorough integration of hierarchical Bayesian inference with comprehensive physical representation of objects.
0
0
0
Thu Sep 18 2014
Artificial Intelligence
Belief revision by examples
A common assumption in belief revision is that the reliability of the information sources is either given, derived from temporal information, or the same for all. This article does not describe a new semantics for integration but the problem of obtaining the reliability. of the sources given the result of a previous merging.
0
0
0