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Visual localization, i.e., the camera pose localization within a known three-dimensional (3D) model, is a basic component for numerous applications such as autonomous driving cars and augmented reality systems. The most widely used methods from the literature are based on local feature matching between a query image that needs to be localized and database images with known camera poses and local features. However, this method still struggles with different illumination conditions and seasonal changes. Additionally, the scene is normally presented by a sparse structure-from-motion point cloud that has corresponding local features to match. This scene representation depends heavily on different local feature types, and changing the different local feature types requires an expensive feature-matching step to generate the 3D model. Moreover, the state-of-the-art matching strategies are too resource intensive for some real-time applications. Therefore, in this paper, we introduce a novel framework called deep-learning accelerated visual localization (DLALoc) based on mesh representation. In detail, we employ a dense 3D model, i.e., mesh, to represent a scene that can provide more robust 2D-3D matches than 3D point clouds and database images. We can obtain their corresponding 3D points from the depth map rendered from the mesh. Under this scene representation, we use a pretrained multilayer perceptron combined with homotopy continuation to calculate the relative pose of the query and database images. We also use the scale consistency of 2D-3D matches to perform the efficient random sample consensus to find the best 2D inlier set for the subsequential perspective-n-point localization step. Furthermore, we evaluate the proposed visual localization pipeline experimentally on Aachen DayNight v1.1 and RobotCar Seasons datasets. The results show that the proposed approach can achieve state-of-the-art accuracy and shorten the localization time about five times.

The output tracking problem is investigated for a nonlinear affine system with multiple modes of continuous control inputs. We convert the family of nonlinear affine systems under consideration into a switched hybrid system by introducing a multiple-valued logic variable. The Fliess functional expansion is adopted to express the input and output relationship of the switched hybrid system. The optimal switching control is determined for a multiple-step output tracking performance index. The proposed approach is applied to a multitarget tracking problem for a flight vehicle aiming for one real target with several decoys flying around it in the terminal guidance course. These decoys appear as apparent targets and have to be distinguished with the approaching of the flight vehicle. The guidance problem of one flight vehicle versus multiple apparent targets should be considered if no large miss distance might be caused due to the limitation of the flight vehicle maneuverability. The target orientation at each time interval is determined. Simulation results show the effectiveness of the proposed method.