Traditional laser localization algorithms such as Monte Carlo localization algorithm have the problems of low accuracy and poor anti-robot kidnapping performance, and traditional two-dimensional code localization algorithms have complex environmental layout and strict limitation to robot's trajectory. In order to solve these problems, a mobile robot localization algorithm based on two-dimensional code vision and laser lidar data was proposed. Firstly, the computer vision technology was used by the robot to detect two-dimensional codes in the test environment, and the poses of detecting two-dimensional codes were transformed to map coordinates respectively, and they were fused to generate the prior pose information. Then the optimized pose was obtained by the point cloud alignment with the generated information as the initial poses. At the same time, the odometry-vision supervising mechanism was introduced to effectively solve the problems brought by the environmental factors such as the information lack of two-dimensional codes and the wrong recognition of the two-dimensional codes as well as ensure the smoothness of the poses. Finally, experimental results based on mobile robot show that, the proposed algorithm has the average error of lidar sampling points reduced by 92%, the average time spent per pose calculation reduced by 88% compared with the classical Adaptive Monto Carlo Localization (AMCL) algorithm, and it solves robot kidnapping problem effectively. This algorithm can be applied to the indoor robots such as storage robot.

To solve the virtual machine deployment problem in Infrastructure as a Service (IaaS) of cloud computing, a virtual machine deployment strategy based on Particle Swarm Optimization (PSO) algorithm was proposed. Since the PSO algorithm has weaknesses of having a slow convergence speed and falling into local optimum easily when dealing with large-scale and complex problems like virtual machine deployment, firstly, a Multiple-population Gaussian Learning Particle Swarm Optimization (MGL-PSO) algorithm was proposed, with using the model of multiple population evolution to accelerate the algorithm convergence, as well as adding Gaussian learning strategy to avoid local optimum. Then according to the deployment model, with using Round Robin (RR) algorithm to initialize the MGL-PSO, a virtual machine deployment strategy aiming to load balancing was proposed. Through the simulation experiment in CloudSim, it validates that MGL-PSO has a higher convergence speed and load imbalance degree is reduced by 13% compared with PSO algorithm. In the two experimental situations, compared with the Opportunistic Load Balancing (OLB) algorithm, the load imbalance degrees of the proposed algorithm decrease by 25% and 15% respectively, and compared with the Greedy Algorithm (GA) the load imbalance degrees decrease by 19% and 7% respectively.

Aiming at the problem of single image degradation and high time complexity of exiting defogging methods under foggy weather, a fast visual optimization defogging algorithm based on atmospheric physical model was proposed. The proposed method firstly used threshold segmentation to find the sky region, and combined with binary tree algorithm to locate global atmospheric light precisely, and then adopted improved constrained least squares filter which can keep the edge detail and reduce noise to optimize original transmittance map. Finally, the fog image could be restored by atmospheric physical model, and the average gradient, information entropy and the visual information fidelity index were adopted to evaluate the image. The experimental results show that compared with the adaptive image enhancement method based on multi-scale Retinex algorithm, the image restoration based on independent component analysis, a quick visual image restoration method and the dark-channel prior de-hazing algorithm, the proposed method has good visual evaluation indexes and strong real-time processing capability.