Departmental Papers (CIS)

Date of this Version


Document Type

Conference Paper


Workshop on Multi-View Geometry in Robotics (MVIGRO 2015) in conjunction with The 2015 Robotics: Science and Systems Conference (RSS 2015), Rome, Italy, July 13-17, 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.

Subject Area

CPS Auto, CPS Embedded Control

Publication Source

Workshop on Multi-View Geometry in Robotics (MVIGRO 2015)

Copyright/Permission Statement

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Date Posted: 16 October 2015

This document has been peer reviewed.