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

Adam Werries and John M. Dolan
Tech. Report, CMU-RI-TR-16-18, Robotics Institute, Carnegie Mellon University, May, 2016

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For autonomous vehicles, navigation systems must be accurate enough to provide lane-level localization. High- accuracy sensors are available but not cost-effective for pro- duction 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.

author = {Adam Werries and John M. Dolan},
title = {Adaptive Kalman Filtering Methods for Low-Cost GPS/INS Localization for Autonomous Vehicles},
year = {2016},
month = {May},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-16-18},
} 2017-09-13T10:38:26-04:00