Aiming at the problems of low information utilization and lack of feature mining capability in multi-sensor fusion networks, this study presents a multi-sensor data fusion-enabled lightweight convolutional double regularization contrast transformer for aerospace bearing small samples fault diagnosis. Firstly, a metric termed integrated cliff entropy is devised to assign weights to vibration signals from diverse sensor channels. It aims to enhance the cyclic impulse characteristics within the fused signals, thereby facilitating more precise fault identification. Secondly, a lightweight Diwaveformer architecture is constructed as the backbone of contrast learning. It enables the global and local features of faulty signals to be comprehensively extracted with less computational effort. Finally, a double contrast loss is constructed to optimize the distribution of intra-class and inter-class features to improve the fault identification ability of the network with small samples. Additionally, a discard regularization method is designed to remove the projection head during the contrast learning process, further advancing the model lightweight. Our method achieved accuracies of 95.54% and 92.56% on two aerospace bearing datasets with extremely sparse training samples, which proved its superior performance.

Inspired by simultaneous localization and mapping (SLAM) style workflow, this article presented an online sequential structure from motion (SfM) solution for high-frequency video and large baseline high-resolution aerial images with high efficiency and novel precision. First, as traditional SLAM systems are not good in processing low overlap images, based on our novel hierarchical feature matching paradigm with multihomography and BoW, we proposed a robust tracking method where the relative pose and its scale are estimated separately followed by a joint optimization by considering both perspective-n-point (PnP) and epipolar constraints. Second, to further optimize the camera poses for the sparse map and dense pointcloud reconstruction, we provided a graph-based optimization with reprojection and GPS constraints, which make the camera trajectory and map georeferenced. We also incrementally generated the dense point cloud in real time from keyframes after local mapping optimization. Finally, we use a publicly available aerial image dataset with sequences of different environments, to evaluate the effectiveness of the proposed method, meanwhile, the robust performance of our solution is demonstrated with applications of high-quality aerial images mosaic and digital surface model (DSM) reconstruction in real time. Compared with the state-of-the-art SLAM and traditional SfM methods, the presented system can output large-scale high-quality ortho-mosaic and DSM in real time with the low computational cost.

Accurate digital orthophoto map generation from high-resolution aerial images is important in various applications. Compared with the existing commercial software and the current state-of-the-art mosaicing systems, a novel fast georeferenced orthophoto mosaicing framework is proposed in this study. The framework can adapt to the challenging requirements of high-accuracy orthoimage generations with relatively fast speed, even if the overlap rate is low. We provide appearance and spatial correlation-constrained fast low-overlap neighbor candidate query and matching. On the basis of GPS information, we introduce an absolute position and rotation-averaging strategy for global pose initialization, which is essential for the high convergence and efficiency of nonconvex pose optimization of every image. We also propose a planar-restricted global pose graph optimization method. The optimization is extremely efficient and robust considering that point clouds are parameterized to planes. Finally, we apply a matching graph-based exposure compensation and region reduction algorithm for large-scale and high-resolution image fusion with high efficiency and novel precision. Experimental results demonstrate that our method can achieve the state-of-the-art performance while maintaining high precision and robustness.

SLAM technology has recently seen many successes and attracted the attention of high-technological companies. However, how to unify the interface of existing or emerging algorithms, and effectively perform benchmark about the speed, robustness and portability are still problems. In this paper, we propose a novel SLAM platform named GSLAM, which not only provides evaluation functionality, but also supplies useful toolkit for researchers to quickly develop their SLAM systems. Our core contribution is an universal, cross-platform and full open-source SLAM interface for both research and commercial usage, which is aimed to handle interactions with input dataset, SLAM implementation, visualization and applications in an unified framework. Through this platform, users can implement their own functions for better performance with plugin form and further boost the application to practical usage of the SLAM.