The Kinematic Models of the SINS and Its Errors on the SE(3) Group in the Earth-Centered Inertial Coordinate System.

In this paper, the kinematic models of the Strapdown Inertial Navigation System (SINS) and its errors on the SE(3) group in the Earth-Centered Inertial frame (ECI) are established. On the one hand, with the ECI frame being regarded as the reference, based on the joint representation of attitude and velocity on the SE(3) group, the dynamic of the local geographic coordinate system (-frame) and the body coordinate system (-frame) evolve on the differentiable manifold, respectively, and the high-order expansion of the Baker-Campbell-Haussdorff equation compensates for the non-commutative motion errors stimulated by strong maneuverability. On the other hand, the kinematics of the left- and right-invariant errors of the -frame and the -frame on the SE(3) group are separately derived, where the errors of the -frame completely depend on inertial sensor errors, while the errors of the -frame rely on position errors and velocity errors.

Regularized least squares locality preserving projections with applications to image recognition.

Locality preserving projection (LPP), as a well-known technique for dimensionality reduction, is designed to preserve the local structure of the original samples which usually lie on a low-dimensional manifold in the real world. However, it suffers from the undersampled or small-sample-size problem, when the dimension of the features is larger than the number of samples which causes the corresponding generalized eigenvalue problem to be ill-posed. To address this problem, we show that LPP is equivalent to a multivariate linear regression under a mild condition, and establish the connection between LPP and a least squares problem with multiple columns on the right-hand side.

Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation.

Unmanned aerial vehicles (UAV) have had significant progress in the last decade, which is applied to many relevant fields because of the progress of aerial image processing and the convenience to explore areas that men cannot reach. Still, as the basis of further applications such as object tracking and terrain classification, semantic image segmentation is one of the most difficult challenges in the field of computer vision. In this paper, we propose a method for urban UAV images semantic segmentation, which utilizes the geographical information of the region of interest in the form of a digital surface model (DSM).