Research On Semantic Mapping And Navigation Method Of Robot

Robots have gradually entered people’s production and life,further improving social productivity and liberating labor.Mobile robots are a class of widely used robots that rely on their own sensors to obtain information about the working environment.Using simultaneous localization and mapping(SLAM)technology and robot navigation technology,they can move autonomously and complete various complex tasks.In the rigid static environment,the traditional SALM method is relatively mature,but it still cannot be stably applied in the complex real environment.In this regard,many semantic SLAM methods have been proposed.By adding semantic information on the basis of traditional SLAM methods,the robustness of positioning and the accuracy of maps are improved to a certain extent.However,most of these methods still rely on low-level features and fail to make full use of high-level semantic features;the created map is composed of a large number of point clouds,and the information density is very low;some methods only focus on the positioning of the robot and ignore the role of the map in robot navigation.practicality.Existing SLAM methods are very different from human self-localization and map drawing modes.Inspired by human behavior patterns and based on the theoretical basis of SLAM,this paper proposes a robot semantic mapping and navigation method.The method achieves robot positioning by constructing semantic target information,constructs semantic maps using semantic points and line segments,and realizes robot navigation on the semantic maps.The main content of the method is as follows:First,the creation of semantic maps.According to the behavior pattern that human beings focus on semantic landmarks while ignoring a large number of environmental details when positioning and describing the environment,the positioning landmarks and environmental structures are decoupled from the existing SLAM method represented by a single point cloud,using semantic points and semantic The line segments construct the semantic roadmap and semantic structure graph respectively to improve the information density of the map.After decoupling,the semantic landmark points are used for robot positioning,and the semantic line segment diagram is used to describe the environment structure.The robot’s positioning is not affected by the change of the environment structure,which reduces the sensitivity of the robot to the dynamic environment.Second,robot navigation based on semantic maps.The positioning of the robot is based on the semantic landmark map,and the particle filter positioning method is used to realize the positioning of the robot based on the sparse semantic landmark points.On the semantic structure diagram composed of line segments,the free space is converted into structured data in the form of triangular unit segmentation,and a three-layer semantic topology diagram is further constructed to endow triangles with address codes in human concepts,thereby efficiently realizing semantics Path planning,and finally transform the semantic path into a specific path that conforms to the robot’s motion characteristics.Finally,a semantic map construction and navigation system for robots is designed,and experiments are carried out on physical robots.In the real environment,the robot is used to collect data sets,and the robot positioning and navigation program is constructed.The effectiveness of the robot’s semantic mapping function and navigation function is verified through experiments.