Markovian jump linear systems-based filtering for visual and GPS aided inertial navigation system

Visual-Inertial SLAM methods have become a very important technology for several applications in robotics. This kind of approach usually is composed by sensors as rate gyros, accelerometers and monocular cameras. Magnetometers and GPS modules generally used for outdoors are absent in the SLAM system...

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Bibliographic Details
Published inProceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 4083 - 4089
Main Authors Inoue, Roberto S., Guizilini, Vitor, Terra, Marco H., Ramos, Fabio
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
Cameras
Global Positioning System
Magnetometers
Mathematical model
Simultaneous localization and mapping
Visualization
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Summary:Visual-Inertial SLAM methods have become a very important technology for several applications in robotics. This kind of approach usually is composed by sensors as rate gyros, accelerometers and monocular cameras. Magnetometers and GPS modules generally used for outdoors are absent in the SLAM system observation, since the magnetometer measurements deteriorate in the presence of ferromagnetic materials and the GPS module signals are unavailable indoors or in urban environments. In order to make use of all these sensors, we propose Markovian jump linear systems (MJLS) to model the modes of operation of the navigation system based on available sensors and their reliability. An extended Kalman filter for MJLS fuses the sensor data and estimates the motion using the best mode of operation for each particular time instant. Experimental results are presented to show the effectiveness of the proposed method, in situations that would pose a challenge for standard data fusion techniques.
ISSN:2153-0866
DOI:10.1109/IROS.2017.8206265