Sensor localization is a central problem for sensor networks. If the sensor positions are uncertain, the target tracking ability of the sensor network is reduced. Sensor localization in underwater environments is traditionally addressed using acoustic range measurements involving known anchor or surface nodes. We explore the usage of triaxial magnetometers and a friendly vessel with known magnetic dipole to silently localize the sensors. The ferromagnetic field created by the dipole is measured by the magnetometers and is used to localize the sensors. The trajectory of the vessel and the sensor positions are estimated simultaneously using an Extended Kalman Filter (EKF). Simulations show that the sensors can be accurately positioned using magnetometers.

The performance of low-cost RTK (real-time kinematic) GPS receivers has been compared to a state-of-theart system as well to each other. Both static and dynamic performances have been compared. The dynamic performance has been evaluated using a vehicle with driving robot on the AstaZero proving ground. The assembly of the low-cost RTK GPS receivers is presented, and the test set-ups described. Besides having a lower data output frequency, two of the lowcost receivers have static and dynamic performance not far from that of the state-of-the-art system.

Abstract-This paper presents a system which combines a zero-velocity-update-(ZUPT-)aided inertial navigation system (INS), using a foot-mounted inertial measurement unit (IMU), with opportunistic use of multi-frequency received signal strength (RSS) measurements. The system does not rely on maps or pre-collected data from surveys of the radio-frequency (RF) environment. Instead it builds its own database of collected RSS measurements during the course of the operation. New RSS measurements are continuously compared with the stored values in the database, and when the user returns to a previously visited area this can thus be detected. This enables loop-closures to be detected online and used for error drift correction. The system utilises a distributed particle simultaneous localization and mapping (DP-SLAM) algorithm which provides a flexible 2D navigation platform that can be extended with more sensors. The experimental results presented in this paper indicates that the developed RSS SLAM algorithm can, in many cases, significantly improve the positioning performance of a foot-mounted INS.

One of the major challenges of automated driving systems (ADS) is showing that they drive safely. Key to ensuring safety is eliciting a complete set of top-level safety requirements (safety goals). This is typically done with an activity called hazard analysis and risk assessment (HARA). In this paper we argue that the HARA of ISO 26262:2018 is not directly suitable for an ADS, both because the number of relevant operational situations may be vast, and because the ability of the ADS to make decisions in order to reduce risks will affect the analysis of exposure and hazards. Instead we propose a tailoring using a quantitative risk norm (QRN) with consequence classes, where each class has a limit for the frequency within which the consequences may occur. Incident types are then defined and assigned to the consequence classes; the requirements prescribing the limits of these incident types are used as safety goals to fulfil in the implementation. The main benefits of the QRN approach are the ability to show completeness of safety goals, and make sure that the safety strategy is not limited by safety goals which are not formulated in a way suitable for an ADS.

Abstract-This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wide band (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping framework, supported by a small number of easy-to-hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment with high precision ground truth. The results show the benefit of the suggested sensor combination and suggest directions for further work.