This paper presents the WiFi-Sensor-for-Robotics (WSR) open-source toolbox111Code: https://github.com/Harvard-REACT/WSR-Toolbox Dataset: https://github.com/Harvard-REACT/WSR-Toolbox-Dataset Demo: https://github.com/Harvard-REACT/WSR-Toolbox/wiki/Demo. It enables robots in a team to obtain relative bearing to each other, even in nonline-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an open-source toolbox. It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS. This has implications for multi-robot mapping and rendezvous, ad-hoc robot networks, and security in multi-robot teams, amongst other applications. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings and for a multi-robot localization use case. Empirical results for hardware experiments show that the bearing estimation from our toolbox achieves accuracy with mean and standard deviation of 1.13 degrees, 11.07 degrees in LOS and 6.04 degrees, 26.4 degrees for NLOS, respectively, in an indoor office environment.

Robotic geo-fencing and surveillance systems require accurate monitoring of objects if/when they violate perimeter restrictions. In this paper, we seek a solution for depth imaging of such objects of interest at high accuracy (few tens of cm) over extended ranges (up to 300 meters) from a single vantage point, such as a pole mounted platform. Unfortunately, the rich literature in depth imaging using camera, lidar and radar in isolation struggles to meet these tight requirements in real-world conditions. This paper proposes Metamoran, a solution that explores long-range depth imaging of objects of interest by fusing the strengths of two complementary technologies: mmWave radar and camera. Unlike cameras, mmWave radars offer excellent cm-scale depth resolution even at very long ranges. However, their angular resolution is at least 10x worse than camera systems. Fusing these two modalities is natural, but in scenes with high clutter and at long ranges, radar reflections are weak and experience spurious artifacts. Metamoran's core contribution is to leverage image segmentation and monocular depth estimation on camera images to help declutter radar and discover true object reflections. We perform a detailed evaluation of Metamoran's depth imaging capabilities in 400 diverse scenarios. Our evaluation shows that Metamoran estimates the depth of static objects up to 90 m away and moving objects up to 305 m away and with a median error of 28 cm, an improvement of 13 x over a naive radar+camera baseline and 23 x compared to monocular depth estimation.

Should you decide to launch a nano-satellite today in Low-Earth Orbit (LEO), the cost of renting ground station communication infrastructure is likely to significantly exceed your launch costs. While space launch costs have lowered significantly with innovative launch vehicles, private players, and smaller payloads, access to ground infrastructure remains a luxury. This is especially true for smaller LEO satellites that are only visible at any location for a few tens of minutes a day and whose signals are extremely weak, necessitating bulky and expensive ground station infrastructure.

Wireless sensing has demonstrated the potential of using Wi-Fi signals to track people and objects, even behind walls.Yet, prior work in this space aims to merely detect the presence of objects around corners, rather than their type. In this paper, we explore the feasibility of the following re-search question: ?Can commodity Wi-Fi radios detect both the location and type of moving objects around them?". We present IntuWition, a complementary sensing system that can sense the location and type of material of objects in the environment, including those out of line-of-sight. It achieves this by sensing wireless signals reflected off surrounding objects using commodity Wi-Fi radios, whose signals penetrate walls and occlusions. At the core of IntuWition is the idea that different materials reflect and scatter polarized waves in different ways. We build upon ideas from RADAR Polarimetry to detect the material of objects across spatial locations, despite mobility of the sensing device and the hardware non-idealities of commodity Wi-Fi radios. A detailed feasibility study reveals an average accuracy of 95% in line-of-sight and 92% in non-line-of-sight in classifying five types of materials:copper, aluminum, plywood, birch, and human. Finally, we present a proof-of-concept application of our system on an autonomous UAV that uses its onboard Wi-Fi radios to sense whether an occlusion is a person versus another UAV.

Low-Power Wide Area Networks (LP-WANs) are an attractive emerging platform to connect the Internet-of-things. LP-WANs enable low-cost devices with a 10-year battery to communicate at few kbps to a base station, kilometers away. But deploying LP-WANs in large urban environments is challenging, given the sheer density of nodes that causes interference, coupled with attenuation from buildings that limits signal range. Yet, state-of-the-art techniques to address these limitations demand inordinate hardware complexity at the base stations or clients, increasing their size and cost.