Applying Deep Learning To Improve Optimization- Based Approaches For Robust Sensor Fusion

Sammanfattning: Recent studies show that deep learning can be employed to learn from sensor data to improve accuracy and robustness of sensor fusion algorithms. In the same vein, in this thesis we use a state-of-the-art temporal convolution network to predict zero velocity updates (ZUPT) from raw inertial measurement unit (IMU) signals, and use the network output to improve the performance of an optimization-based pose estimator. Experiments were conducted on publicly available datasets, and results show that (i) the network can distinguish a car in motion vs. a car standing still by observing an IMU signal, and (ii) that ZUPT detection enhances the observability of states in the optimization-based pose estimation, thus reducing local drift.