We present the design, implementation, and evaluation of MiFly, a self-localization system for autonomous drones that works across indoor and outdoor environments, including low-visibility, dark, and GPS-denied settings. MiFly performs 6DoF self-localization by leveraging a single millimeter-wave (mmWave) anchor in its vicinity - even if that anchor is visually occluded. MiFly's core contribution is in its joint design of a mmWave anchor and localization algorithm. The low-power anchor features a novel dual-polarization dual-modulation architecture, which enables single-shot 3D localization. Mm Wave radars mounted on the drone perform 3D localization relative to the anchor and fuse this data with the drone's internal inertial measurement unit (IMU) to estimate its 6DoF trajectory. We implemented and evaluated MiFly on a DJI drone. We collected over 6,600 localization estimates across different trajectory patterns and demonstrate a median localization error of 7 cm and a 90thpercentile less than 15 cm, even in low-light conditions and when the anchor is fully occluded (visually) from the drone. Demo video: voutu.be/LfXfZ26tEok

This paper presents the design, implementation, and evaluation of mmNorm, a new and highly-accurate method for non-line-of-sight 3D object reconstruction using millimeter wave (mmWave) signals. In contrast to past approaches for millimeter-wave-based imaging that perform backprojection for 3D object reconstruction, mmNorm reconstructs the surface by estimating the object's surface normals. To do this, it introduces a novel algorithm that directly estimates the surface normal vector field from mmWave reflections. By then inverting the normal field, it can reconstruct structural isosurfaces, then solve for the exact surface through a novel mmWave optimization framework.

We present the design, implementation, and evaluation of RFlect, a mmWave imaging system capable of producing around-the-corner high-resolution images in practical environments. RFlect leverages signals reflected off complex surfaces (e.g., poles, concave surfaces, or composition of multiple surfaces) to image objects that are not in the RF line-of-sight. RFlect models the reflections and introduces reconstruction algorithms for different types of surfaces. It also leverages a novel method for precisely mapping the location and geometry of the reflecting surface. We also derive the theoretical resolution and coverage for different reflecting surface geometries. We built a prototype of RFlect and performed extensive evaluations to demonstrate its ability to reconstruct the shape of objects around the corner, with an average Chamfer Distance of 2cm and 3D F-Score of 88.6%.

There is much interest in fine-grained RFID localization systems. Existing systems for accurate localization typically require infrastructure, either in the form of extensive reference tags or many antennas (e.g., antenna arrays) to localize RFID tags within their radio range. Yet, there remains a need for fine-grained RFID localization solutions that are in a compact, portable, mobile form, that can be held by users as they walk around areas to map them, such as in retail stores, warehouses, or manufacturing plants.

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