Use of Dempster-Shafer Conflict Metric to Detect Interpretation
  Inconsistency
  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).
