Localization is a critical task for underwater robots, yet today's underwater localization systems are limited by their accuracy, scalability, and/or energy consumption (i.e., longevity). We present the design, implementation, and evaluation of EchoBLUE- an accurate, scalable, and low-power localization system for underwater robots.

We present the design, implementation, and evaluation of 3D-BLUE, an ultra-low-power underwater 3D localization system that can be deployed on compact robots to accurately localize them in shallow underwater environments. 3D-BLUE’s design introduces two core components. First, it adapts a recent ultra-low-power underwater acoustic communication technology (called piezo-electric backscatter) to the underwater robotics localization problem; specifically, it integrates backscatter nodes into the underwater robot and uses them for localizing it. Second, it leverages the physical properties of the backscatter technology to efficiently extract spatio-temporal-spectral features from the backscatter signal; using these features, it devises a particle-filter-based algorithm to localize the corresponding robot accurately in challenging shallow-water environments. We implemented an end-to-end prototype of 3D-BLUE on a BlueROV2 robot and custombuilt backscatter localization system, and evaluated it in dozens of experimental trials in a pool. Our results demonstrate that 3D-BLUE can localize the robot with an accuracy of around 0.25m at close range and an accuracy of around 1.4m at a range of 10m. This high localization accuracy opens important commercial, naval, and environmental applications in challenging shallow-water environments such as shores, rivers, pools, and narrow waterways.

This demo presents SCOPE, an electric scooter equipped with RF sensing and License Plate Recognition (LPR) capabilities to achieve accurate and efficient parking enforcement. SCOPE leverages Ultra High Frequency (UHF) Radio Frequency IDentification (RFID) technology and incorporates the Synthetic Aperture Radar (SAR)-based RFID localization algorithm for precise parking spot recognition. Our demo presents an end-to-end system that combines License Plate Recognition (LPR) and RF-based positioning, achieving 88% accuracy in matching vehicles to their lots and enabling automated parking enforcement in practical environments. Demo Video: youtu.be/EuaiJqOeA5Y. Implementation: [Source Code].

This demo presents X-AR, an Augmented Reality headset that enables its user to find and retrieve hidden items. X-AR leverages battery-less 3-cent Radio Frequency IDentification (RFID) tags that are already deployed on billions of items. As a user wearing X-AR moves in an environment, the headset transmits RF signals and leverages natural human mobility to locate RFID-tagged items. X-AR then guides the user toward the desired item for retrieval. We built a real-time prototype of this system on a Microsoft Hololens 2 AR headset with a conformal antenna, software radios, and an edge server. Our demo will enable any user to wear our X-AR prototype and use it to find and retrieve hidden items in a warehouse-like setting. Demo Video: youtu.be/bdUN21ft7G0