Uncertainty minimization in multi-sensor localization systems using model selection theory

Belief propagation methods are the state-of-the-art with multi-sensor state localization problems. However, when localization applications have to deal with multi-modality sensors whose functionality depends on the environment of operation, we understand the need for an inference framework to identify confident and reliable sensors. Such a framework helps eliminate failed/non-functional sensors from the fusion process minimizing uncertainty while propagating belief. We derive a framework inspired from model selection theory and demonstrate results on real world multi-sensor robot state localization and multi-camera target tracking applications.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Andreas Koschan, David L. Page, Hamparsum Bozdogan, Mongi A. Abidi, Sreenivas R. Sukumar
• Keywords: Computer Vision, ICPR 2008, Multi-modality Sensors, Multi-sensor Robot State, Multi-sensor State Localization