Information Gain in Object Recognition Via Sensor Fusion

We have been studying information theoretic measures, entropy and mutual information , as performance metrics on the information gain given a standard suite of sensors. Object pose is described by a single angle of rotation using a Lie group parameterization; observations are generated using CAD models for the targets of interest and simulators. Variability in the data due to the sensor by which the scene is remotely observed is statistically characterized via the data likelihood function. Given observations from multiple sensors, data fusion is automatic in the posterior density. We consider the mutual information between the target pose and remote observation as a performance measure in the pose estimation context. We have quantitatively examined the additional information gains due to sensor fusion. Furthermore , we relate the information theoretic performance measures with probability of error in the pose estimation problem via Fano's classic inequality .