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

Autor(es)

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

• Tipo de documento: Artigo Científico
• Fonte: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS); v. N/A, p. 7-pg., 2017-01-01.

Resumo

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)

• Processo FAPESP: 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente
• Beneficiário: Marco Henrique Terra
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 15/18085-8 - Sistema de Reconstrução 3D Aéreo em Tempo-Real e de Baixo Custo para Ambientes Externos baseado em Filtro de Kalman Robusto Markoviano
• Beneficiário: Roberto Santos Inoue
• Modalidade de apoio: Bolsas no Exterior - Pesquisa
• Processo FAPESP: 14/08432-0 - Sistema de referência de atitude, orientação e posição baseado em Filtro de Kalman robusto implementado em FPGA
• Beneficiário: Marco Henrique Terra
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Regular