Online Robot Dead Reckoning Localization Using Maximum Relative Entropy Optimization With Model Constraints
Renaldas Urniežius, Ali Mohammad‐Djafari, Jean‐François Bercher, Pierre Bessìère · AIP conference proceedings · 2011
The principle of Maximum relative Entropy optimization was analyzed for dead reckoning localization of a rigid body when observation data of two attached accelerometers was collected. Model constraints were derived from the relationships between the sensors. The experiment’s results confirmed that accelerometers each axis’ noise can be successfully filtered utilizing dependency between channels and the dependency between time series data. Dependency between channels was used for a priori calculation, and a posteriori distribution was derived utilizing dependency between time series data. There was revisited data of autocalibration experiment by removing the initial assumption that instantaneous rotation axis of a rigid body was known. Performance results confirmed that such an approach could be used for online dead reckoning localization.