Generative Adversarial Networks for Shadow Removal to Improve Semantic Segmentation for Autonomous Driving

Traversability estimation is a key component in autonomous driving tasks. In many applications, semantic segmentation is used to pixel-wise classify a visual scene. The pixel-wise segmented map is used to estimate the traversability of different environments. The semantic segmentation accuracy can drop if environmental conditions change. The...

Hybrid Semantic Mapping for Autonomous Off-Road Driving

Unmanned Ground Vehicle (UGV) navigation in unstructured off-road environments can benefit from accurate traversability estimation. Often, experiments with UGVs use semantic segmentation networks for visual scene understanding. Based on the pixel-wise classification of a semantic segmentation network, the UGV can distinguish traversable from non...

Tensor Network Kalman Filter for Large-Scale MIMO Systems: With Application to Adaptive Optics

For large-scale system with tens of thousands of states and outputs the computation in the conventional Kalman filter becomes time-consuming such that Kalman filtering in large-scale real-time application is practically infeasible. A possible mathematical framework to lift the curse of dimensionality is to lift the problem in higher dimensions...

Radial basis functions for aberration retrieval in adaptive optics

The use of radial basis function (RBF) expansion as a means to represent the generalized pupil function for aberration retrieval from intensity point-spread function has been investigated in this research article. The optimal choice of RBF hyper-parameters is derived empirically to achieve an increased accuracy of approximation along with well...