Centimeter-level positioning accuracy provided by real-time kinematic (RTK) systems can support emerging IoT applications that require high-resolution spatial monitoring and precise geo-referenced sensor reporting. However, the high data rate requirements of traditional RTK protocols prevent their use in IoT networks with limited communication capacity and regulatory transmission constraints, such as LoRaWAN (Long Range Wide Area Network). We propose an RTK-over-LoRaWAN architecture that significantly reduces the data volume required for RTK positioning through adaptive RTCM scheduling. We conduct extensive field experiments with baseline distances up to 10km to validate that our method maintains centimeter-level accuracy under sparse RTCM delivery. The results show that RTK initialization requires only 500 B to 1000 B of total downlink payload, while extended RTK tracking is sustained with an effective downlink throughput below 15 B/s, reducing data requirements to under 5% compared to conventional RTK implementations. Our approach meets regulatory constraints such as those defined for the EU868 region, achieving duty cycles well below 1% per hour and enabling the practical deployment of RTK-based positioning in LoRaWAN networks.

Navigating autonomous underwater vehicles (AUVs) in shallow and harbor waters is challenging and typically has higher accuracy requirements than navigation in the open sea. We investigate enhancements to underwater localization techniques based on Two-Way Ranging (TWR) using acoustic modems, which have great potential to meet localization accuracy requirements at lower cost and complexity than current systems. By modifying the Extended Kalman Filter, we account for dynamic positioning errors that occur during the movement of the localization target, i.e., the underwater vehicle, and the fact that distance measurements with acoustic modems are delayed in time. The method is evaluated numerically and experimentally showing an accuracy improvement of about 20 cm compared to the traditional EKF scheme. In real-world tests at ranges below 30 m, the absolute localization accuracy is assessed using an RTK-GPS reference, showing that a positioning error below 35 cm can be achieved in a quasi-static test, while in a dynamic test the tracking error is mostly below 75 cm.

Considering realistic characteristics of acoustic localization methods is crucial for roboticists when developing guidance and control algorithms for small and agile underwater robots. Current simulators either rely purely on geometric distancing, i.e. do not consider dynamic effects such as robot motion during acoustic signal propagation, or they are too complex for usage by non-communication experts and, thus, vulnerable to misconfiguration. We propose an open ROS-based framework that extends existing robot simulators (e. g. Gazebo) by simulating the effects of realistic acoustic ranging for underwater robot localization. Thus, our simulator enables realistic real-time analysis and evaluation of guidance, navigation, and control algorithms in software in-the-loop systems. For this purpose, we incorporate and encapsulate the non-trivial characteristics of acoustic communication and ranging such as robot motion during signal propagation, packet reception failure, and modem timings. This ensures the applicability of the tool by roboticists who are typically non-experts in acoustic communication and guarantees accurate and realistic simulation results. We demonstrate the functionality and performance of our framework and validate it on real-world experimental data on the example of a two-way ranging method. Our open-source release includes well-defined interfaces and parameters as well as a tutorial. This targets other roboticists who can either use our framework directly or easily adapt it to their individual setup, e. g., by adding further acoustic-ranging protocols.