In this work we propose, on a conceptual level, a 2-D localization method based on Qualitative Spatial Reasoning (QSR). QSR is a formal way to reason about spatial entities in the environment and their relationship. We use QSR framework as observations that connect observer's position with the environment using a predefined map. These observations are, per definition, qualitative and do not contain quantitative numerical values. We develop a way of incorporating these observations in the Bayesian filter setting, mainly by creating a fitting likelihood function. The main motivation for this work comes from a case where a person wants to localize itself in the environment, but without any concrete quantitative measurements. We demonstrate the method with a small simulation example showing that it can produce a fairly good position estimate compared to the case without support from qualitative observations, i.e., pure dead-reckoning. The comparison is based on Monte Carlo evaluation. We also suggest future improvements of the method with extension to 3-D case, that can be useful for flying platforms, as well as the possibility of building the map concurrently with localization.

It is shown how dense optical flow obtained using deep learning can be used to provide high quality visual odometry. The obtained odometric information can be utilized as a component to reduce the inherent drift of inertial navigation systems (INS). This could be a key component to provide autonomous system with robust localization capability in GNSS denied environments. The method leverages the power of estimating optical flow from neural networks, which can provide reliable results even when feature based optical flow fails. Comparison of different methods to decide which points of the dense optical flow that should be used to provide as good visual odometry as possible has been performed. Furthermore, it is exemplified how the methodology can help limit the drift in an INs.

Network-centric multitarget tracking under communication constraints is considered, where dimension-reduced track estimates are exchanged. Previous work on target tracking in this subfield has focused on fusion aspects only and derived optimal ways of reducing dimensionality based on fusion performance. In this work we propose a novel problem formalization where estimates are reduced based on association performance. The problem is analyzed theoretically and problem properties are derived. The theoretical analysis leads to an optimization strategy that can be used to partly preserve association quality when reducing the dimensionality of communicated estimates. The applicability of the suggested optimization strategy is demonstrated numerically in a multitarget scenario.

We consider a decentralized sensor network of multiple nodes with limited communication capability where the cross-correlations between local estimates are unknown. To reduce the bandwidth the individual nodes determine which subset of local information is the most valuable from a global perspective. Three information selection methods (ISM) are derived. The proposed ISM require no other information than the communicated estimates. The simulation evaluation shows that by using the proposed ISM it is possible to determine which subset of local information is globally most valuable such that both reduced bandwidth and high performance are achieved.

This paper presents three iterative methods for orientation estimation. The first two are based on iterated Extended Kalman filter (IEKF) formulations with different state representations. The first is using the well-known unit quaternion as state (q-IEKF) while the other is using orientation deviation which we call IMEKF. The third method is based on nonlinear least squares (NLS) estimation of the angular velocity which is used to parametrise the orientation. The results are obtained using Monte Carlo simulations and the comparison is done with the non-iterative EKF and multiplicative EKF (MEKF) as baseline. The result clearly shows that the IMEKF and the NLS-based method are superior to q-IEKF and all three outperform the non-iterative methods.