A 3D Space Formulation of a Navigation Kalman Filter for Autonomous Vehicles

Abstract

The Kalman Filter has many applications in mobile robotics ranging from perception to position estimation to control. This report formulates a navigation Kalman Filter. That is, one which estimates the position of autonomous vehicles. The filter is developed according to the state space formulation of Kalman's original papers. The state space formulation is particularly appropriate for the problem of vehicle position estimation. This filter formulation is fairly general. This generality is possible because the problem has been addressed: -in 3D -in state space, with an augmented state vector -asynchronously -with tensor calculus measurement models The formulation has wide ranging uses. Some of the applications include: -as the basis of a vehicle position estimation system, whether any or all of dead reckoning, triangulation, or terrain aids or other landmarks are used. -as the dead reckoning element and overall integration element when INS or GPS is used -as the mechanisms for map matching in mapping applications -as the identification element in adaptive control applications It can perform these functions individually or all at once. The filter is formulated for a general redundant asynchronous sensor suite. It provides a single place for the integration of every sensor on a autonomous vehicle, and the measurement models for most of them are included. All sensors provide indirect measurements of state and any number can be accommodated. The filter subsumes many applications of the Kalman filter to mobile robot navigation problems as special cases. It complements the RANGER vehicle controller which is the subject of another technical report that appears later in this series.