Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter

In this paper we present a robust location estimation algorithm especially focused on the accuracy in vertical position. A loosely-coupled error state space Kalman filter, which fuses sensor data of an Inertial Measurement Unit and the output of a Global Positioning System device, is augmented by height information from an altitude measurement unit. This unit consists of a barometric altimeter whose output is fused with topographic map information by a Kalman filter to provide robust information about the current vertical user position. These data replace the less reliable vertical position information provided the GPS device. It is shown that typical barometric errors like thermal divergences and fluctuations in the pressure due to changing weather conditions can be compensated by the topographic map information and the barometric error Kalman filter. The resulting height information is shown not only to be more reliable than height information provided by GPS. It also turns out that it leads to better attitude and thus better overall localization estimation accuracy due to the coupling of spatial orientations via the Direct Cosine Matrix. Results are presented both for artificially generated and field test data, where the user is moving by car.