{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/123642"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/123642","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Luo, Zhihong"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Program in Media Arts and Sciences (Massachusetts Institute of Technology)","school":null,"contributors":[],"advisors":["Fadel Adib."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:23Z","subjects":["Program in Media Arts and Sciences"],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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