ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Master of Science in Computer Science


Computer Science and Engineering

First Advisor

Manfred Huber


Robots rely on sensors to map their surroundings. As a result, the accuracy of the map depends heavily on the sensor noise and in particular on accurate knowledge of it. The common way to minimize the impact of sensor noise is to use filtering algorithms. Accuracy of these filtering algorithms (like the Kalman filter) relies on the accuracy of the user supplied measurement noise model. Inaccurate noise models lead to higher residual noise in state estimates and errors in the estimate of the precision of the state estimate. It is therefore important to have precise noise models and thus accurately calibrated sensors. Most current methods for estimating noise models require a knowledge of 'ground truth' labels for sensor data and often require either to remove the sensor from the system or the presence of particular, sensor-specific calibration targets. This method can be expensive and require modifications to the system or the environment. In this research, we present a method for estimating noise models for multiple sensors without prior knowledge of ground truth and without the use of calibration targets. In contrast, this method takes advantage of identifiable targets in the environment to calibrate sensors against each other using a sensor noise consistency measure based on KL Divergence. This algorithm can be run periodically to update model estimates in unforeseen environments.


Sensor calibration, KL divergence, Perception, Machine learning, Bayesian filtering, Noise model, Sensor noise model


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington