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Summary: Target localization in unknown environment is one of the development directions of mobile robots. Simultaneous localization and mapping (SLAM) can be used to build maps in unknown environments, but it has the problem of poor readability and interactivity. In this article, target detection and SLAM are combined to search and locate the target by using rich RGBD images information. The determined position in the global map is conducive to the follow‐up operation of the target by mobile robots. By establishing a local dense point cloud map of the target object, the current state of the target object is directly displayed, the readability of the map is improved, and the disadvantages of difficult understanding of the global sparse map and slow construction of the global dense map are avoided. A target localization algorithm under the framework of yolov4 is designed to apply in the process of SLAM global mapping. Our works are helpful for obtaining positions of objects in three‐dimensional space. The experimental results show that the time‐consuming of this method in dense mapping is reduced by 50%–70%, and the number of point clouds is also reduced by 60%–70%. [ABSTRACT FROM AUTHOR]

Summary: The feature layers of different layers in the single shot multibox detector (SSD) are independently used as the input of the classification network, so it is easy to detect the same object. This article proposes an improved SSD model based on deep feature fusion. In the SSD algorithm, the deep feature fusion between the target detection layer and its adjacent feature layer is used, including convolution kernels and pooling kernels of different sizes, down‐sampling of low‐level features and up‐sampling of deconvolution of high‐level features. The network is improved by combining the target frame recommendation strategy in the SSD algorithm and the frame regression algorithm. The experimental results show that the improved SSD algorithm improves the detection accuracy and detection rate of the target, and the effect is more obvious for the relatively small‐scale target. [ABSTRACT FROM AUTHOR]

In order to research the hysteretic behavior of T-stub beam-column semi-rigid connections, six full-scale T-stub semi-rigid connection specimens were designed for quasi-static testing. Five parameters were considered, such as beam height, diameter of bolt, size of T-stub connecting piece, bolt number, as well as column section area. And the influences of the parameters on the loading process, failure patterns, hysteretic behavior, and ductility behavior were analyzed. ABAQUS was used to establish FEM model of T-stub connections. The numerical simulations were compared with test results, and the causes of error were analyzed. Results show that: T-stub semi-rigid connections have excellent deformation capacity, the rotations of all connections are more than 0.03 rad, and can meet the standard of semi-rigid connection; the ductility performance of connections is good, with each specimen displacement ductility coefficient greater than 3; hysteresis curve at the later loading stage is typical Z type; compared with end-plate semirigid connections, the energy dissipation coefficient is smaller; size of T-stub connecting pieces and bolt number of connections are the principal factor for initial stiffness, ultimate bearing capacity and deformation capacity; the influence of the beam height is less; the numerical simulation results and test results are consistent with each other; finite element model which considers material cyclic plasticity can well predict the mechanical properties of connections under cyclic loading. [ABSTRACT FROM AUTHOR]