Adaptive Kalman Filtering Methods for Low-Cost GPS/INS Localization for Autonomous Vehicles

Details

• Creators:
  Werries, Adam ; Dolan, John
• Corporate Creators:
  Carnegie-Mellon University. Robotics Institute
• Corporate Contributors:
  Technologies for Safe and Efficient Transportation. University Transportation Center ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Automobile navigation systems
  • Advanced vehicle control systems
  • Kalman filtering
  • Intelligent vehicles
  • Special purpose vehicles
  • Inertial navigation systems
  • Inertial Navigation Systems
  • GPS
  • autonomous vehicles
  • sensors
  More +
• Publication/ Report Number:
  CMU-RI-TR-16-18
• DOI:
  https://doi.org/10.1184/R1/6551687
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  v.1
• Corporate Publisher:
  Carnegie-Mellon University
• Abstract:
  For autonomous vehicles, navigation systems must be accurate enough to provide lane-level localization. High-accuracy sensors are available but not cost-effective for production use. Although prone to significant error in poor circumstances, even low-cost GPS systems are able to correct Inertial Navigation Systems (INS) to limit the effects of dead reckoning error over short periods between sufficiently accurate GPS updates. Kalman filters (KF) are a standard approach for GPS/INS integration, but require careful tuning in order to achieve quality results. This creates a motivation for a KF which is able to adapt to different sensors and circumstances on its own. Typically for adaptive filters, either the process (Q) or measurement (R) noise covariance matrix is adapted, and the other is fixed to values estimated a priori. We show that by adapting Q based on the state-correction sequence and R based on GPS receiver-reported standard deviation, our filter reduces GPS root-mean-squared error by 23% in comparison to raw GPS, with 15% from only adapting R.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/36294/dot_36294_DS1.pdf
• File Type:
  [PDF - 890.61 KB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:17f4637a4e8610df3b11d9dc11f1ea13ecea83781ed3eace5595f829320f328fef7b62fb77f01df3dfff49d73bbb106da35b85431f8231d91eb08ca7fd5c3fc3