Visual simultaneous localization and mapping (SLAM), based on point features, achieves high localization accuracy and map construction. They primarily perform simultaneous localization and mapping based on static features. Despite their efficiency and high precision, they are prone to instability and even failure in complex environments. In a dynamic environment, it is easy to keep track of failures and even failures in work. The dynamic object elimination method, based on semantic segmentation, often recognizes dynamic objects and static objects without distinction. If there are many semantic segmentation objects or the distribution of segmentation objects is uneven in the camera view, this may result in feature offset and deficiency for map matching and motion tracking, which will lead to problems, such as reduced system accuracy, tracking failure, and track loss. To address these issues, we propose a novel point-line SLAM system based on dynamic environments. The method we propose obtains the prior dynamic region features by detecting and segmenting the dynamic region. It realizes the separation of dynamic and static objects by proposing a geometric constraint method for matching line segments, combined with the epipolar constraint method of feature points. Additionally, a dynamic feature tracking method based on Bayesian theory is proposed to eliminate the dynamic noise of points and lines and improve the robustness and accuracy of the SLAM system. We have performed extensive experiments on the KITTI and HPatches datasets to verify these claims. The experimental results show that our proposed method has excellent performance in dynamic and complex scenes.

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With the current popularity of Electric Vehicles (EV), especially in some critical EV applications such as hospital EV fleets, the demand for continuous and reliable power supply is increasing. However, most of the charging stations are powered in a centralized way, which causes transistors and other components to be subjected to high voltage and current stresses that reduce reliability, and a single point of failure can cause the entire system to fail. Therefore, a significant effort is made in this paper to improve the reliability of the charging system. In terms of charging system structure design, a distributed charging structure with fault tolerance is designed and a mathematical model to evaluate the reliability of the structure is proposed. In terms of control, a current sharing control algorithm is designed that can achieve fault tolerance. To further improve the reliability of the system, a thermal sharing control method based on current sharing technology is also designed. This method can improve the reliability of the charging system by distributing the load more rationally according to the differences in component performance and operating environment; FPGA-based control techniques are provided, and innovative ideas of pipeline control and details of mathematical reasoning for key IP cores are presented. Experiments show that N + 1 redundancy fault tolerance can be achieved in both current sharing and thermal sharing modes. In the current sharing experiment, when module 3 failed, the total current only fluctuated 800 mA within 500 ms, which is satisfactory. In the thermal sharing experiment, after module 3 failed, modules 1, 2, and 4 adjusted the current reasonably under the correction of the thermal sharing loop, and the total current remained stable throughout the process. The experimental results prove that the charging system structure design and control method proposed in this paper are feasible and excellent.

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