Information-Based Georeferencing by Dual State Iterated Extended Kalman Filter with Implicit Measurement Equations and Nonlinear Geometrical Constraints

EasyChair Preprint 3577

Abstract

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.

Keyphrases: 6-DOF, Dual State, Geometrical constraints, Kalman filtering, MSS, Monte Carlo simulation, georeferencing, iterated extended Kalman filter

@booklet{EasyChair:3577,
  author    = {Rozhin Moftizadeh and Johannes Bureick and Sören Vogel and Ingo Neumann and Hamza Alkhatib},
  title     = {Information-Based Georeferencing by Dual State Iterated Extended Kalman Filter with Implicit Measurement Equations and Nonlinear Geometrical Constraints},
  howpublished = {EasyChair Preprint 3577},
  year      = {EasyChair, 2020}}