4 papers
Neural Inertial Odometry from Lie Events
Royina Karegoudra Jayanth, Yinshuang Xu, Evangelos Chatzipantazis +2
Neural displacement priors (NDP) can reduce the drift in inertial odometry and provide uncertainty estimates that can be readily fused with off-the-shelf filters. However, they fai…
Improving Equivariant Model Training via Constraint Relaxation
Stefanos Pertigkiozoglou, Evangelos Chatzipantazis, Shubhendu Trivedi +1
Equivariant neural networks have been widely used in a variety of applications due to their ability to generalize well in tasks where the underlying data symmetries are known. Desp…
Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
Georgios Mentzelopoulos, Evangelos Chatzipantazis, Ashwin G. Ramayya +5
Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining dat…
EqNIO: Subequivariant Neural Inertial Odometry
Royina Karegoudra Jayanth, Yinshuang Xu, Ziyun Wang +3
Neural networks are seeing rapid adoption in purely inertial odometry, where accelerometer and gyroscope measurements from commodity inertial measurement units (IMU) are used to re…