Multi-Sensor-System (MSS) georeferencing is a challenging task in engineering that should be dealt with in the most accurate way possible. The easiest and most straightforward way for this purpose is to rely on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. However, at indoor environments or crowded inner-city areas, such data are not accurate to be entirely relied on. Therefore, appropriate filtering algorithms are required to compensate for possible errors and to improve the accuracy of the results. Sometimes it is also possible to increase the functionality of a filtering technique by engaging additional complementary information that can directly influence the outputs. Such information could be, e.g. geometrical features of the environment in which the MSS runs through. The current paper deals with MSS georeferencing by means of a Dual State Iterated Extended Kalman Filter (DSIEKF) that is based on an efficient combination of the Iterated Extended Kalman Filter (IEKF) with implicit measurement equations technique and nonlinear geometrical constraints. Final results of such an algorithm are shown to be satisfactory not only from the accuracy point of view but also the computation time.

In this paper, a novel set-membership Kalman filter is applied on a data set which is obtained from a real world experiment. In this experiment, taken from the scope of georeferencing of terrestrial laser scanner, a multi-sensor system has captured the trajectory of two GNSS antennas. The dynamical system contains the random uncertainty and set-membership uncertainty simultaneously. Both estimated results from classic extended Kalman filter and novel set-membership Kalman filter are shown and compared. Detailed analysis of the set-membership Kalman filter is given in the end.

Accurate, reliable and complete georeferencing with kinematic multi-sensor systems (MSS) is very demanding if common types of observations (e.g. usually GNSS) are imprecise or completely absence. The main reasons for this are challenging areas of indoor applications or inner-city areas with shadowing and multipath effects. However, those complex and tough environments are rather the rule than the exception. Consequently, we are developing an information-based georeferencing approach which can still estimate precise and accurate pose parameters when other current methods may fail. We modified an iterated extended Kalman filter (IEKF) approach which can deal with implicit measurement equations and introduced nonlinear equality constraints for the state parameters to integrate additional information. Hence, we can make use of geometric circumstances in the direct environment of the MSS and provide a more precise and reliable georeferencing.