Fusão Sensorial com sensores Inerciais de Baixo Desempenho e Odômetro para Estimar Coordenadas Geodésicas em Ambientes sem Sinal GPS (Sensor Fusion with Low-Grade Inertial Sensors and Odometer to Estimate Geodetic Coordinates in Environments without GPS Signal)

Douglas Daniel Sampaio Santana (douglas.santana@usp.br), Celso Massatoshi Furukawa (cmfuruka@usp.br), Newton Maruyama (maruyama@usp.br)


Escola Politécnica da Universidade de São Paulo - EPUSP
This paper appears in: Revista IEEE América Latina

Publication Date: June 2013
Volume: 11,   Issue: 4 
ISSN: 1548-0992


Abstract:
This paper presents a sensor fusion algorithm based on a Kalman Filter to estimate geodetic coordinates and reconstruct a car test trajectory in environments where there is no GPS signal. The sensor fusion algorithm is based on low-grade strapdown inertial sensors (i.e. accelerometers and gyroscopes) and an incremental odometer, from which, velocity measurements is obtained. Since the dynamic system is non linear, an Extended Kalman Filter (EKF) is used to estimate the states (i.e. latitude, longitude and altitude) and reconstruct the test trajectory. The relevance of this work is given by the fact that, in the current literature, much has been published about the merger Inertial Sensors and GPS, however, currently no literature that addresses the form of sensor fusion proposed here is available. Another aspect that could be emphasized is that the proposed algorithm has potential to be applied in environments where GPS signals are not available, such as Pipeline Inspection Gauge (PIG) as depicted below in figure 2. The inertial navigation system developed and tested, shows that only with inertial sensors measurements, a closed tested trajectory can not be reconstructed satisfactorily, however when it uses the sensor fusion, the trajectory can be reconstructed with relative success. On preliminary experiments, it was possible reconstruct a closed trajectory of approximately 2800m, attaining a final error of 13m.

Index Terms:
Inertial Navigation, Inertial Sensors, Sensor Fusion, Kalman Filter, Terrestrial Navigation.   


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