Visual localization and mapping system is affected by dynamic objects in a dynamic environment， so that it has increase of localization and mapping errors and decrease of robustness. And motion segmentation of input images can significantly improve the performance of visual localization and mapping system in dynamic environment. Dynamic objects in dynamic environment can be divided into moving objects and potential moving objects. Current dynamic object recognition methods have problems of chaotic moving subjects and poor real-time performance. Therefore， motion segmentation strategies of visual localization and mapping system in dynamic environment were reviewed. Firstly， the strategies were divided into three types of methods according to preset conditions of the scene： methods based on static assumption of image subject， methods based on prior semantic knowledge and multi-sensor fusion methods without assumption. Then， these three types of methods were summarized， and their accuracy and real-time performance were analyzed. Finally， aiming at the difficulty of balancing accuracy and real-time performance of motion segmentation strategy of visual localization and mapping system in dynamic environment， development trends of the motion segmentation methods in dynamic environment were discussed and prospected.

In relation extraction tasks， distant supervision is a common method for automatic data labeling. However， this method will introduce a large amount of noisy data， which affects the performance of the model. In order to solve the problem of noisy data， a relation extraction method based on negative training and transfer learning was proposed. Firstly， a noisy data recognition model was trained through negative training method. Then， the noisy data were filtered and relabeled according to the predicted probability value of the sample， Finally， a transfer learning method was used to solve the domain shift problem existing in distant supervision tasks， and the precision and recall of the model were further improved. Based on Thangka culture， a relation extraction dataset with national characteristics was constructed. Experimental results show that the F1 score of the proposed method reaches 91.67%， which is 3.95 percentage points higher than that of SENT （Sentence level distant relation Extraction via Negative Training） method， and is much higher than those of the relation extraction methods based on BERT （Bidirectional Encoder Representations from Transformers）， BiLSTM+ATT（Bi-directional Long Short-Term Memory and Attention）， and PCNN （Piecewise Convolutional Neural Network）.

In order to solve the problem that most exploration/exploitation strategies of reinforcement learning ignore the risk brought by the agent action selection with random components in exploration process， a Q-table initialization approach based on Factorization Machine （FM） was proposed for safe exploration. Firstly， the explored Q-values were introduced as prior knowledge， and then FM was used to build the model of potential interaction between states and actions in the prior knowledge. Finally， the unknown Q-values in Q-table were predicted based on this model to further guide the exploration of the agents. A/B testing was conducted in the grid reinforcement learning environment Cliffwalk of OpenAI Gym. The number of bad exploration episodes of Boltzmann and Upper Confidence Bound （UCB） exploration/exploitation strategies based on the proposed approach are reduced by 68.12% and 89.98% respectively. Experimental results show that the proposed approach improves the safety of exploration， and accelerates the convergence at the same time.

The designing principle of large data driver buffer was studied. Ordinary data transfer strategies have their limitations either in time efficiency or performance stability. A multithreading positive waiting strategy based on dynamic feedback was proposed. Firstly, multithreading data transfer scheme was implemented in the form of main thread's positive wait. For high performance guarantees under unpredictable workloads condition, dynamic feedback was imported to balance the data and instruction transfer between server-to-driver and driver-to-client.