Date of this Version
Radoslav Ivanov, Nikolay Atanasov, Miroslav Pajic, Insup Lee, and George Pappas, "Robust Localization Using Context-Aware Filtering", Workshop on Multi-View Geometry in Robotics (MVIGRO 2015) . July 2015.
In this paper we develop a robot localization technique that incorporates discrete context measurements, in addition to standard continuous state measurements. Context measurements provide binary information about detected events in the robot’s environment, e.g., a building is recognized using image processing or a known radio station is received. Such measurements can only be detected from certain positions and can, therefore, be correlated with the robot’s state. We investigate two specific examples where context measurements are especially useful – an urban localization scenario where GPS measurements are not reliable as well as the capture of the RQ-170 Sentinel drone in Iran, where GPS measurements were spoofed. By focusing on a specific class of probability of context detection functions, we derive a closed-form Gaussian mixture filter that is precise, captures context, and has the theoretical properties of the Kalman filter. Finally, we provide simulations of the urban localization scenario with an unmanned ground vehicle and show that the proposed context-aware filter is more robust and more accurate than the conventional extended Kalman filter, which only uses continuous measurements.
CPS Auto, CPS Embedded Control
Workshop on Multi-View Geometry in Robotics (MVIGRO 2015)
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date Posted: 16 October 2015
This document has been peer reviewed.