Fast Initialization Method Of SLAM Based On Features Points

In the era of big data and artificial intelligence,computer vision has become increasingly popular as part of artificial intelligence.The visual SLAM system enables the robots to locate its position and create virtual maps according to vision sensors,so the visual SLAM is regarded as artificial intelligence in robots.Now it is widely used in many fields such as mobile robot,unmanned aerial vehicle,smart car and augmented reality.This project aims at the problem of complicated procedures and slow speeds in the initialization of the traditional visual SLAM system.The project studies from two aspects: the fast initialization of the SLAM system based on the traditional indoor model and the combination of deep learning and other technologies to initialize the SLAM system quickly.The main work of this paper is as follow:1.The related basic principles are introduced,such as the camera models,camera calibration and correct distortion,fundamental matrix,essential matrix,homography matrix,the process and the related technologies of the SLAM system are also introduced.2.This paper studies the fast initialization of SLAM system based on indoor model.This paper mainly introduces the process of the fast initialization of SLAM system,including the detection of vanishing points,the detection of orientation map and the generation of indoor model assumptions.This paper presents a method to select the best indoor model and reconstruct the indoor scene to quickly initialize the SLAM system.This improves the initialization of SLAM system and ensures the stability of the system.This paper does lots of experiments on the datasets and proves that the method is efficient and useful.3.This paper studies the deep learning and segmentation based on graph techniques and proposes new methods for the selection of indoor model.This paper mainly introduces the depth estimation using deep learning and the segmentation based on the graph.It proposes that using the state and relative position information of the plane in space to calculate the accuracy of the indoor model.And achieved the desired effect in the experiments.We reconstruct the code and use it in the initialization part of SLAM system,then do some experiments.The result shows that the method is practicality.