Learning a Better Attitude: A Recurrent Neural Filter for Orientation Estimation
Typ
Examensarbete för masterexamen
Program
Systems, control and mechatronics (MPSYS), MSc
Publicerad
2020
Författare
Fransson, Amanda
Lundström, Oscar
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In the current paradigm of sensor fusion orientation estimation from inertial measurements
unit sensor data is done using techniques derived with Bayesian statistics.
These derivations are based on assumptions about noise distributions and hand
crafted equations describing the relevant system dynamics. Machine learning, and
more specifically neural networks, may provide an alternate solution to the problem
of orientation estimation where no assumptions or handcrafted relationships
are present. This thesis aims to investigate whether a neural network-based filter
can achieve a performance comparable to, or exceeding that of, the more conventional
extended Kalman filter. Two network architectures based on long short-term
memory layers are proposed, trained, evaluated and compared using data from the
Oxford inertial odometry dataset. Of the two suggested model architectures the socalled
recurrent neural filter is found to give a the better performance. The recurrent
neural filter has a structure inspired by Bayesian filtering, with a prediction and an
update step, allowing it to output a prediction in the event of missing data. Further,
the evaluated models are trained to estimate orientation as well as a parameterized
error covariance matrix. Our results show that the suggested recurrent neural filter
outperforms the benchmark filter both in average root mean square error and in
execution time. The result also indicates that the machine learning-based approach
for sensor fusion problems may be an attractive alternative to hand crafted filters
in the future.
Beskrivning
Ämne/nyckelord
sensor-fusion , state estimation , absolute orientation estimation , recurrent neural filter , recurrent neural network , RNN , LSTM , IMU , MARG