3D backscatter localization for fine-grained robotics

Abstract

This thesis presents the design and implementation of TurboTrack, a 3D localization system for fine-grained robotic tasks. TurboTrack's unique capability is that it can localize backscatter nodes with sub-centimeter accuracy without any constraints on their locations or mobility. TurboTrack makes two key technical contributions. First, it presents a pipelined architecture that can extract a sensing bandwidth from every single backscatter packet that is three orders of magnitude larger than the backscatter communication bandwidth. Second, it introduces a Bayesian space-time superresolution algorithm that combines time series of the sensed bandwidth across multiple antennas to enable accurate positioning. Our experiments show that TurboTrack simultaneously achieves a median accuracy of sub-centimeter in each of the x/y/z dimensions and a 99th percentile latency less than 7.5 milliseconds in 3D localization. This enables TurboTrack's real-time prototype to achieve fine-grained positioning for agile robotic tasks, as we demonstrate in multiple collaborative applications with robotic arms and nanodrones including indoor tracking, packaging and assembly, and handover.