4 accepted papers
Implementation of safe and efficient autonomous driving systems requires accurate prediction of the long-term trajectories of surrounding vehicles. High uncertainty in traffic behavior makes it difficult to predict trajectories in urban environments, which have various road geometries. To over-come…
We propose an uncertainty-based sensor fusion framework for visual-inertial odometry, which is the task of estimating relative motion using images and measurements from inertial measurement units. Visual-inertial odometry enables robust and scale-aware estimation of motion by incorporating sensor st…
Several machine learning tasks require to represent the data using only a sparse set of interest points. An ideal detector is able to find the corresponding interest points even if the data undergo a transformation typical for a given domain. Since the task is of high practical interest in computer…
This paper deals with deep neural networks for predicting accurate dense disparity map with Semi-global matching (SGM). SGM is a widely used regularization method for real scenes because of its high accuracy and fast computation speed. Even though SGM can obtain accurate results, tuning of SGM's pen…