Semantic Optimization of Feature-Based SLAM

The purpose of this paper is to provide reasonable recommendation and removal of inappropriate information for SLAM (Simultaneous Localization and Mapping) technology based on feature method. The methodology is to propose a semantic recognition of environment objects in the natural scene through object detection, which is a kind of bag of word method in SLAM problem between the key frames and object level, the method of establishing key frames, and the relationship between the target object levels, through the practical signicance of the target object level to judge the merits of the target object level information, and then combined with key frames in the visual SLAM relations with relevant information, so as to get object level targets in each key frame and the relationship between the relevant information, so as to achieve through the object level semantic information to judge the merits of the key frames and screening, as well as to the key frames to judge the merits of the relevant information and screening purpose. The finding of the study is the above method can retain the information of high reliability and good stability for visual SLAM and process the key frames with poor reliability or low stability and the information related to key frames.