In this paper, methods for estimating one's own position from a video image are considered. A robust two-stage algorithm for reconstructing the scene structure from its observed video images is proposed. In the proposed algorithm, at the feature extraction and matching stage, a random sample based on the neighborhood graph cuts is used to select the most probable matching feature pairs. At the nonlinear optimization stage, an improved optimization algorithm with an adaptive attenuation coefficient and dynamic adjustment of the trust region is used. Compared with the classical Levenberg-Marquard (LM) algorithm, global and local convergence can be better balanced. To simplify the system's decisions, the Schur complement method is used at the group tuning stage, which allows for a significant reduction in the amount of computation. The experiments confirmed the operability and effectiveness of the proposed algorithm.

Keywords: 3D reconstruction，graph-cut, Structure-from-Motion (SfM)，RANSAC，Bundle Adjustment optimization，Levenberg-Marquardt algorithm，Robust feature matching

The paper considers a lightweight modified version of the YOLO-v5 neural network, which is used to recognize road scene objects in the task of controlling an unmanned vehicle. In the proposed model, the pooling layer is replaced by the ADown module in order to reduce the complexity of the model. The C2f module is added as a feature extraction module to improve accuracy by combining features. Experiments using snowy road scenes are presented and the effectiveness of the proposed model for object recognition is demonstrated.

Keywords: Bobkov A. V., Du K., Dai I., Wang Z., Chen H.