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RoVaR: Robust Multi-agent Tracking through Dual-layer Diversity in Visual and RF Sensor Fusion

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RoVaR: Robust Multi-agent Tracking through Dual-layer Diversity in Visual and RF Sensor Fusion The plethora of sensors in our commodity devices provides a rich substrate for sensor-fused tracking. Yet, today’s solutions are unable to deliver robust and high tracking accuracies across multiple agents in practical, everyday environments – a feature central to the future of immersive and collaborative applications. This can be attributed to the limited scope of diversity leveraged by these fusion solutions, preventing them from catering to the multiple dimensions of accuracy, robustness (diverse environmental conditions) and scalability (multiple agents) simultaneously.In this work, we take an important step towards this goal by introducing the notion of dual-layer diversity to the problem of sensor fusion in multi-agent tracking. We demonstrate that the fusion of complementary tracking modalities, – passive/relative (e.g. visual odometry) and active/absolute tracking (e.g.infrastructure-assisted RF localization) offer a key first layer of diversity that brings scalability while the second layer of diversity lies in the methodology of fusion, where we bring together the complementary strengths of algorithmic (for robustness) and data-driven (for accuracy) approaches. ROVAR is an embodiment of such a dual-layer diversity approach that intelligently attends to cross-modal information using algorithmic and data-driven techniques that jointly share the burden of accurately tracking multiple agents in the wild. Extensive evaluations reveal ROVAR’S multi-dimensional benefits in terms of tracking accuracy, scalability and robustness to enable practical multi-agent immersive applications in everyday environments.

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Mosaic: Leveraging Diverse Reflector Geometries for Omnidirectional Around-Corner Automotive Radar

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Mosaic: Leveraging Diverse Reflector Geometries for Omnidirectional Around-Corner Automotive Radar A large number of traffic collisions occur as a result of obstructed sight lines, such that even an advanced driver assistance system would be unable to prevent the crash. Recent work has proposed the use of around-the-corner radar systems to detect vehicles, pedestrians, and other road users in these occluded regions. Through comprehensive measurement, we show that these existing techniques cannot sense occluded moving objects in many important real-world scenarios. To solve this problem of limited coverage, we leverage multiple, curved reflectors to provide comprehensive coverage over the most important locations near an intersection. In scenarios where curved reflectors are insufficient, we evaluate the relative benefits of using additional flat planar surfaces. Using these techniques, we more than double the probability of detecting a vehicle near the intersection in three real urban locations and enable NLoS radar sensing using an entirely new class of reflectors.

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RoVaR: Robust Multi agent Tracking through Dual layer Diversity in Visual and RF Sensor Fusion

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RoVaR: Robust Multi agent Tracking through Dual layer Diversity in Visual and RF Sensor Fusion The plethora of sensors in our commodity devices provides a rich substrate for sensor fused tracking. Yet, today’s solutions are unable to deliver robust and high tracking accuracies across multiple agents in practical, everyday environments a feature central to the future of immersive and collaborative applications. This can be attributed to the limited scope of diversity leveraged by these fusion solutions, preventing them from catering to the multiple dimensions of accuracy, robustness (diverse environmental conditions) and scalability (multiple agents) simultaneously. In this work, we take an important step towards this goal by introducing the notion of dual layer diversity to the problem of sensor fusion in multi agent tracking. We demonstrate that the fusion of complementary tracking modalities, passive/relative (e.g., visual odometry) and active/absolute tracking (e.g., infrastructure assisted RF localization) offer a key first layer of diversity that brings scalability while the second layer of diversity lies in the methodology of fusion, where we bring together the complementary strengths of algorithmic (for robustness) and data driven (for accuracy) approaches. RoVaR is an embodiment of such a dual layer diversity approach that intelligently attends to cross modal information using algorithmic and data driven techniques that jointly share the burden of accurately tracking multiple agents in the wild. Extensive evaluations reveal RoVaR’s multi dimensional benefits in terms of tracking accuracy (median of 15cm), robustness (in unseen environments), light weight (runs in real time on mobile platforms such as Jetson Nano/TX2), to enable practical multi agent immersive applications in everyday environments.

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SkyHAUL: A Self-Organizing Gigabit Network In The Sky

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SkyHAUL: A Self-Organizing Gigabit Network In The Sky We design and build SkyHaul, the first large-scale, self-organizing network of Unmanned Aerial Vehicles (UAVs) that are connected using a mm Wave wireless mesh backhaul. While the use of a mmWave backhaul paves the way for a new class of bandwidth-intensive, latency-sensitive cooperative applications (e.g. LTE coverage during disasters), the network of UAVs allows these applications to be executed at operating ranges that are far beyond the line-of-sight distances that limit individual UAVs today.To realize the challenging vision of deploying and maintaining an airborne, mm Wave mesh backhaul that caters to dynamic applications, SkyHaul’s design incorporates various elements: (i) Role-specific UAV operations that simultaneously address application tracking and backhaul connectivity (ii) Novel algorithms to jointly address the problem of deployment (position, yaw of UAVs) and traffic routing across the UAV network, and (iii)A provably optimal solution for fast and safe reconfiguration of UAV backhaul during application dynamics. We evaluate the performance of SkyHaul through both real-world UAV flight operations as well as large scale simulations.

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SpaceBeam: LiDAR-Driven One-Shot mmWave Beam Management

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SpaceBeam: LiDAR-Driven One-Shot mmWave Beam Management mmWave 5G networks promise to enable a new generation of networked applications requiring a combination of high throughput and ultra-low latency. However, in practice, mmWave performance scales poorly for large numbers of users due to the significant overhead required to manage the highly-directional beams. We find that we can substantially reduce or eliminate this overhead by using out-of-band infrared measurements of the surrounding environment generated by a LiDAR sensor. To accomplish this, we develop a ray-tracing system that is robust to noise and other artifacts from the infrared sensor, create a method to estimate the reflection strength from sensor data, and finally apply this information to the multiuser beam selection process. We demonstrate that this approach reduces beam-selection overhead by over 95% in indoor multi-user scenarios, reducing network latency by over 80% and increasing throughput by over 2× in mobile scenarios.

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Redefining Passive in Backscattering with Commodity Devices

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Redefining Passive in Backscattering with Commodity Devices The recent innovation of frequency-shifted (FS) backscatter allows for backscattering with commodity devices, which are inherently half-duplex. However, their reliance on oscillators for generating the frequency-shifting signal on the tag, forces them to incur the transient phase of the oscillator before steady-state operation. We show how the oscillator’s transient phase can pose a fundamental limitation for battery-less tags, resulting in significantly low bandwidth efficiencies, thereby limiting their practical usage.To this end, we propose a novel approach to FS-backscatter called xSHIFT that shifts the core functionality of FS away from the tag and onto the commodity device, thereby eliminating the need for on-tag oscillators altogether. The key innovation in xSHIFT lies in addressing the formidable challenges that arise in making this vision a reality. Specifically, xSHIFT’s design is built on the construct of beating twin carrier tones through a non-linear device to generate the desired FS signal – while the twin RF carriers are generated externally through a careful embedding into the resource units of commodity WiFi transmissions, the beating is achieved through a carefully-designed passive tag circuitry. We prototype xSHIFT’s tag, which is the same form factor as RFID Gen 2 tags, and characterize its promising real-world performance. We believe xSHIFT demonstrates one of the first, truly passive tag designs that has the potential to bring commodity backscatter to consumer spaces.

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SkyHaul: An Autonomous Gigabit Network Fabric In The Sky

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SkyHaul: An Autonomous Gigabit Network Fabric In The Sky We design and build SKYHAUL, the first large scale, autonomous, self organizing network of Unmanned Aerial Vehicles (UAVs) that are connected using a mmWave wireless mesh backhaul. While the use of a mmWave backhaul paves the way for a new class of bandwidth intensive, latency sensitive cooperative applications (e.g., LTE coverage during disasters, surveillance during rescue in challenging terrains), the network of UAVs allows these applications to be executed at operating ranges that are far beyond the line of sight distances that limit individual UAVs today. To realize the challenging vision of deploying and maintaining an airborne mmWave mesh backhaul to cater to dynamic applications, SKYHAUL’s design incorporates various elements: (1) Role specific UAV operations that simultaneously address application tracking and backhaul connectivity (2) Novel algorithms to jointly address the problem of deployment (position, yaw of UAVs) and traffic routing across the UAV network, and (3) A provably optimal solution for fast and safe reconfiguration of UAV backhaul during application dynamics. We implement SKYHAUL on four DJI Matrice 600 Pros to demonstrate its practicality and performance through autonomous flight operations, complemented by large scale simulations.

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