Learning a Better Attitude: A Recurrent Neural Filter for Orientation Estimation

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dc.contributor.authorFransson, Amanda
dc.contributor.authorLundström, Oscar
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.examinerForsberg, Peter
dc.contributor.supervisorBerglind, Niclas
dc.date.accessioned2020-06-21T11:42:56Z
dc.date.available2020-06-21T11:42:56Z
dc.date.issued2020sv
dc.date.submitted2019
dc.description.abstractIn 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.sv
dc.identifier.coursecodeMMSX30sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/300918
dc.language.isoengsv
dc.relation.ispartofseries2020:16sv
dc.setspec.uppsokTechnology
dc.subjectsensor-fusionsv
dc.subjectstate estimationsv
dc.subjectabsolute orientation estimationsv
dc.subjectrecurrent neural filtersv
dc.subjectrecurrent neural networksv
dc.subjectRNNsv
dc.subjectLSTMsv
dc.subjectIMUsv
dc.subjectMARGsv
dc.titleLearning a Better Attitude: A Recurrent Neural Filter for Orientation Estimationsv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
local.programmeSystems, control and mechatronics (MPSYS), MSc
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