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A model of the world built from sensor data may be incorrect even if thesensors are functioning correctly. Possible causes include the use ofinappropriate sensors (e.g. a laser looking through glass walls), sensorinaccuracies accumulate (e.g. localization errors), the a priori models arewrong, or the internal representation does not match the world (e.g. a staticoccupancy grid used with dynamically moving objects). We are interested in thecase where the constructed model of the world is flawed, but there is no accessto the ground truth that would allow the system to see the discrepancy, such asa robot entering an unknown environment. This paper considers the problem ofdetermining when something is wrong using only the sensor data used toconstruct the world model. It proposes 11 interpretation inconsistencyindicators based on the Dempster-Shafer conflict metric, Con, and evaluatesthese indicators according to three criteria: ability to distinguish trueinconsistency from sensor noise (classification), estimate the magnitude ofdiscrepancies (estimation), and determine the source(s) (if any) of sensingproblems in the environment (isolation). The evaluation is conducted using datafrom a mobile robot with sonar and laser range sensors navigating indoorenvironments under controlled conditions. The evaluation shows that the Gambinoindicator performed best in terms of estimation (at best 0.77 correlation),isolation, and classification of the sensing situation as degraded (7% falsenegative rate) or normal (0% false positive rate).

Use of Dempster-Shafer Conflict Metric to Detect Interpretation Inconsistency