This paper focuses on 3D localization of transmitting satellites in low Earth orbits (LEO). 3D localization of transmitters in low orbits is an important emerging problem for many applications such as spectrum management, orbit determination, and backup for GPS failures in orbit. We present StarLoc - a system to geolocate transmitters in space using a combination of orbital modeling and a new interferometric 3D angle-of-arrival estimation technique. StarLoc's design relies on a unique insight - the motion of satellites is governed by orbital dynamics and is therefore along a 2D manifold in a 3D space. This reduces the degrees of freedom in satellite motion and allows us to 3D-locate and track a satellite with just three antennas in a 2D plane. We evaluate StarLoc using signal transmissions from 81 Starlink satellites. Our results show that StarLoc can estimate the 3D-angle of a satellite within 0.7° and the orbital range within 5 km. Our dataset and implementation are available at: https://connectedsystemslab.github.io/starloc.

Accurate, ubiquitous indoor localization has long been a central goal in wireless systems, yet most proposed methods remain impractical for large-scale deployment. We present PeepLoc, a scalable Wi-Fi-based system that leverages existing infrastructure and unmodified mobile devices. PeepLoc operates in any indoor space with standards-compliant Wi-Fi APs and regular pedestrian traffic. It combines (a) extracting non-cooperative time-of-flight (ToF) from any AP, and (b) a crowdsourced bootstrapping approach using pedestrian dead reckoning (PDR) to localize APs as anchors. Implemented on commodity hardware, PeepLoc is evaluated across four buildings, achieving 3.41m mean and 3.06m median error, outperforming commercial indoor localization systems and approaching GPS-level accuracy outdoors.

Millimeter-wave (mmWave) radar is increasingly being considered as an alternative to optical sensors for robotic primitives like simultaneous localization and mapping (SLAM). While mmWave radar overcomes some limitations of optical sensors, such as occlusions, poor lighting conditions, and privacy concerns, it also faces unique challenges, such as missed obstacles due to specular reflections or fake objects due to multipath. To address these challenges, we propose Radarize, a self-contained SLAM pipeline that uses only a commodity single-chip mmWave radar. Our radar-native approach uses techniques such as Doppler shift-based odometry and multipath artifact suppression to improve performance. We evaluate our method on a large dataset of 146 trajectories spanning 4 buildings and mounted on 3 different platforms, totaling approximately 4.7 Km of travel distance. Our results show that our method outperforms state-of-the-art radar and radar-inertial approaches by approximately 5x in terms of odometry and 8x in terms of end-to-end SLAM, as measured by absolute trajectory error (ATE), without the need for additional sensors such as IMUs or wheel encoders.

Unmanned aerial vehicles (UAVs) rely on optical sensors such as cameras and lidar for autonomous operation. However, such optical sensors are error-prone in bad lighting, inclement weather conditions including fog and smoke, and around textureless or transparent surfaces. In this paper, we ask: is it possible to fly UAVs without relying on optical sensors, i.e., can UAVs fly without seeing? We present BatMobility, a lightweight mmWave radar-only perception system for UAVs that eliminates the need for optical sensors. BatMobility enables two core functionalities for UAVs - radio flow estimation (a novel FMCW radar-based alternative for optical flow based on surface-parallel doppler shift) and radar-based collision avoidance. We build BatMobility using commodity sensors and deploy it as a real-time system on a small off-the-shelf quadcopter running an unmodified flight controller. Our evaluation1 shows that BatMobility achieves comparable or better performance than commercial-grade optical sensors across a wide range of scenarios.

Implantable and edible medical devices promise to provide continuous, directed, and comfortable healthcare treatments. Communicating with such devices and localizing them is a fundamental, but challenging, mobile networking problem. Recent work has focused on leveraging near field magnetism-based systems to avoid the challenges of attenuation, refraction, and reflection experienced by radio waves. However, these systems suffer from limited range, and require fingerprinting-based localization techniques. We present InnerCompass, a magnetic backscatter system for in-body communication and localization. InnerCompass relies on new magnetism-native design insights that enhance the range of these devices. We design the first analytical model for magnetic-field-based localization, that generalizes across different scenarios. We've implemented InnerCompass and evaluated it in porcine tissue. Our results show that Inner-Compass can communicate at 5 Kbps at a distance of 25 cm, and localize with an accuracy of 5 mm.

Wireless tags are increasingly used to track and identify common items of interest such as retail goods, food, medicine, clothing, books, documents, keys, equipment, and more. At the same time, there is a need for labelled visual data featuring such items for the purpose of training object detection and recognition models for robots operating in homes, warehouses, stores, libraries, pharmacies, and so on. In this paper, we ask: can we leverage the tracking and identification capabilities of such tags as a basis for a large-scale automatic image annotation system for robotic perception tasks? We present RF-Annotate, a pipeline for autonomous pixel-wise image annotation which enables robots to collect labelled visual data of objects of interest as they encounter them within their environment. Our pipeline uses unmodified commodity RFID readers and RGB-D cameras, and exploits arbitrary small-scale motions afforded by mobile robotic platforms to spatially map RFIDs to corresponding objects in the scene. Our only assumption is that the objects of interest within the environment are pre-tagged with inexpensive battery-free RFIDs costing 3–15 cents each. We demonstrate the efficacy of our pipeline on several RGB-D sequences of tabletop scenes featuring common objects in a variety of indoor environments.