Markovian Jump Linear Systems-based filtering for Visual and GPS Aided Inertial Navigation System

Author(s)

Inoue, Roberto S. ; Guizilini, Vitor ; Terra, Marco H. ; Ramos, Fabio ; Bicchi, A ; Okamura, A

• Document type: Journal article
• Source: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS); v. N/A, p. 7-pg., 2017-01-01.

Abstract

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. (AU)

• FAPESP's process: 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment
• Grantee: Marco Henrique Terra
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 15/18085-8 - Low-Cost Aerial 3D Reconstruction System in Real-Time for Outdoor Environments based on Markovian Robust Kalman Filte
• Grantee: Roberto Santos Inoue
• Support Opportunities: Scholarships abroad - Research
• FAPESP's process: 14/08432-0 - Attitude and heading reference system based on recursive robust Kalman filter implemented in FPGA
• Grantee: Marco Henrique Terra
• Support Opportunities: Research Grants - eScience and Data Science Program - Regular Program Grants