All-Weather Sub-50-cm Radar-Inertial Positioning

Details

• Alternative Title:
  Tight-coupling of Vision, Radar, and Carrier-phase Differential GNSS for Robust All-weather Positioning Project Title, from cover
• Creators:
  Narula, Lakshay ; Iannucci, Peter A. ; Humphreys, Todd E.
• Corporate Creators:
  University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Algorithms
  • Autonomous vehicles
  • Data fusion
  • Positioning
  • Radar
  • Sensors
  • Weather
  • Inertial Measurement Units (IMU)
  • Localization
  • All-weather
  More +
• Publication/ Report Number:
  D-STOP/2020/154
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP)
• Description:
  Deployment of automated ground vehicles beyond the confines of sunny and dry climes will require sub-lane level positioning techniques based on radio waves rather than near-visible-light radiation. Like human sight, lidar and cameras perform poorly in low-visibility conditions. This paper develops and demonstrates a novel technique for robust sub-50-cmaccurate urban ground vehicle positioning based on all-weather sensors. The technique incorporates a computationally-efficient globally-optimal radar scan batch registration algorithm into a larger estimation pipeline that fuses data from commercially available low-cost automotive radars, low-cost inertial sensors, vehicle motion constraints, and, when available, precise GNSS measurements. Performance is evaluated on an extensive and realistic urban data set. Comparison against ground truth shows that during 60 min of GNSS-denied driving in the urban center of Austin, TX, the technique maintains 95th-percentile errors below 50 cm in horizontal position and 0:5° in heading.
• Format:
  PDF
• Funding:
  DTRT13-G-UTC58
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/55864/dot_55864_DS1.pdf
• File Type:
  [PDF - 7.10 MB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:ee0358f4acee901df4d8c7becf04c67667612b3def2a301b96f2ca11b8a19dd5ab253dacb6de4a6248e598ce22269401ae372338b63985b852157913c7db586c