We are glad to share our pre-print "MountNet: Learning an Inertial Sensor Mounting Angle with Deep Neural Networks"
Finding the mounting angle of a smartphone inside a car is crucial for navigation, motion detection, activity recognition, and other applications.
It is a challenging task in several aspects: (i) the mounting angle at the drive start is unknown and may differ significantly between users; (ii) the user, or bad fixture, may change the mounting angle while driving; (iii) a rapid and computationally efficient real-time solution is required for most applications.
The proposed model, MountNet, uses only IMU readings as input and, in contrast to existing solutions, does not require inputs from global navigation satellite systems (GNSS).
IMU data is collected for training and validation with the sensor mounted at a known yaw mounting angle and a range of ground truth labels is generated by applying a prescribed rotation to the measurements.
