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 custom-built 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].

We present RL2, a robotic system for efficient and accurate localization of UHF RFID tags. In contrast to past robotic RFID localization systems, which have mostly focused on location accuracy, RL2 learns how to jointly optimize the accuracy and speed of localization. To do so, it introduces a reinforcement-learning-based (RL) trajectory optimization network that learns the next best trajectory for a robot-mounted reader antenna. Our algorithm encodes the aperture length and location confidence (using a synthetic-aperture-radar formulation) from multiple RFID tags into the state observations and uses them to learn the optimal trajectory. We built an end-to-end prototype of RL2 with an antenna moving on a ceiling-mounted 2D robotic track. We evaluated RL2 and demonstrated that with the median 3D localization accuracy of 0.55m, it locates multiple RFID tags 2.13x faster compared to a baseline strategy. Our results show the potential for RL-based RFID localization to enhance the efficiency of RFID inventory processes in areas spanning manufacturing, retail, and logistics.

Locating RFID-tagged items in the environment and guiding humans to retrieve the tagged items is an important problem in the RFID community. This paper explores how to exploit synergies between Augmented Reality (AR) headsets and RFID localization to help solve this problem by improving both user experience and localization accuracy. Using fundamental mathematical formulations for RFID localization, we derive confidence metrics and display guidance to the user to improve their experience and enable them to retrieve items faster. We build our primitives into an end - to-end system, RF - AR, and show that it achieves 8.6 cm median localization accuracy within 76 seconds and enables 55% faster retrieval than state-of-the-art past systems. Our results demonstrate that AR-based “human-in-the-loop” designs can make the localization task more accurate and efficient, and thus holds the potential to improve processes where items need to be retrieved quickly, such as in manufacturing, retail, and warehousing.

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

We present a 3D positioning system called Pokeball, which uses a single-source magnetic sphere. The system comprises three mutually orthogonal coils and an Arbitrary Waveform Generator (AWG) to generate the designated signals. By applying Frequency Division Multiplexing (FDM), Pokeball generates two rotating magnetic fields with different frequencies of phase-quadrature current signals. The positioning object is equipped with a three-axis magnetoresistive sensor that measures the strength of the magnetic field and extracts the results into two signals with different frequencies. The phases of the two signals are used to determine the elevation and azimuth angles, while the amplitudes of the signals are used to calculate the distance between the source and the object. Then, based on the calculations, the location of the object is determined. The results of a comprehensive set of experiments demonstrate that Pokeball can achieve an accuracy of less than 40 cm for positioning errors in its effective positioning range. Moreover, Pokeball does not require site surveys, and it is robust against radio interference and environmental obstructions. Our approach has a great deal of potential for use in a wide range of applications, such as mobile systems, wearable computing devices, and location-based applications, where an instantaneous and accurate indoor 3D positioning system is essential.

No abstract available.