详情

原文传递 Adaptive Kalman Filtering Methods for Low-Cost GPS/INS Localization for Autonomous Vehicles.

题名：	Adaptive Kalman Filtering Methods for Low-Cost GPS/INS Localization for Autonomous Vehicles.
作者：	Dolan, J. M.; Werries, A.
关键词：	Global position system (GPS), Vehicle safety, Vehicle to vehicle communications, Autonomous land vehicles, Intelligent transportation systems, Intelligent vehicles, Vehicle detectors
摘要：	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 to limit the effects of dead reckoning error over short periods between sufficiently accurate GPS updates. Kalman filters are a standard approach for GPS/INS integration, but require careful tuning in order to achieve quality results. This creates a motivation for a Kalman filter 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 of Kalman filters is adapted, and the other is fixed to values estimated a priori. We show that intelligently adapting both matrices in an intelligent manner can provide a more accurate navigation solution.
报告类型：	科技报告