3D Backscatter Localization for Fine-Grained Robotics

3D Backscatter Localization for Fine-Grained Robotics

Zhihong Luo, Qiping Zhang, Yunfei Ma, Manish Singh, and Fadel Adib. 16th USENIX Symposium on Networked Systems Design and
Implementation (NSDI 19).

This paper presents the design, implementation, and evaluation 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 super-resolution 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, assembly, and handover.