Graduation Year


Document Type




Degree Granting Department

Computer Science and Engineering

Major Professor

Robin R. Murphy, Ph.D.

Co-Major Professor

Lawrence Hall, Ph.D.

Committee Member

Gita Sukthankar, Ph.D.

Committee Member

Rahul Tripathi, Ph.D.

Committee Member

Zenon Medina-Cetina, Ph.D.


uncertainty handling, robotics, Dempster-Shafer, sensing metrics, sensor suitability


While exploring an unknown environment, an intelligent agent has only its sensors to guide its actions. Each sensor's ability to provide accurate information depends on the environment's characteristics. If the agent does not know these characteristics, how can it determine which sensors to rely on? This problem is exacerbated by sensing anomalies: cases where sensor(s) are working but the readings lead to an incorrect interpretation of the environment, e.g. laser sensors cannot detect glass. This work addresses the following research question: Can an inconsistency-based sensing accuracy indicator, which relies solely on fused sensor readings, be used to detect and characterize sensing anomalies in unknown environments?

A novel inconsistency-based approach was investigated for sensing anomaly detection and characterization by a mobile robot using range sensing for mapping. Based on the hypothesis that sensing anomalies manifest as inconsistent sensor readings, the approach employed Dempster-Shafer theory and six metrics from the evidential literature to measure the magnitude of inconsistency. These were applied directly to fused sensor data with a threshold, forming an indicator, used to distinguish minor noise from anomalous readings.

Experiments with real sensor data from four indoor and two outdoor environments showed that three of the six evidential inconsistency metrics can partially address the issue of noticing sensing anomalies in unknown environments. Polaroid sonar sensors, SICK laser range finders, and a Canesta range camera were used. Despite extensive training in known environments, the indicators could not reliably detect sensing anomalies, i.e. distinguish them from ordinary noise. However, sensing accuracy could be estimated (correlations with sensor error exceeded 0.8) and regions with suspect readings could be isolated. Trained indicators failed to rank sensors, but improved map quality by resetting suspect regions (up to 57.65%) or guiding sensor selection (up to 75.86%).

This work contributes to the robotics and uncertainty in artificial intelligence communities by establishing the use of evidential metrics for adapting a single sensor or identifying the most accurate sensor to optimize the sensing accuracy in unknown environments. Future applications could enable intelligent systems to switch information sources to optimize mission performance and identify the reliability of sources for different environments.