Research And Implementation Of Indoor Mobile Robot VSLAM Based On Point-Line Feature Fusion

In recent years,indoor robots are widely used in medical,blind steering,cleaning,and another fields.Simultaneous As the core technologies of golem autonomy,Localization and Mapping(SLAM)technology have perpetually been a probe hotspot.VSLAM is employed to extract purpose options within the setting,then perform consequent positioning and mapping work.However,the indoor setting is usually artificial sofas,tables,chairs,floors,etc.,and also the purpose options are unit comparatively distributed,leading to poor mapping and positioning accuracy of the SLAM rule that depend on point features.In order to cut back the positioning error of the golem within the indoor setting,the thesis adds line options on the idea of purpose options and constructs a visual SLAM system that integrates purpose and line options.The most analytical contents of the thesis area unit as following:First of all,thesis explains the connection between the relevant coordinate systems and therefore the camera model;and introduces the imaging model and dealing principle of the RGB-D camera;analyzes the kinds and causes of camera distortion and therefore the corresponding process ways,and calibrates the camera,get the camera's internal parameters and distortion coefficient;mathematically specific the SLAM downside,and recommend the SLAM framework of this text on the classic SLAM;Secondly,thesis extracts the point and line feature and describes the feature at the same time in the visual odometry,then uses the brute force method to match,and adopts the corresponding mismatch elimination method for the difference of the point and line feature.In order to solve the problem of a large number of broken lines on the same straight line due to the over-segmentation of line features,which affects the calculation efficiency,thesis combines the over-segmented line features by sequentially performing angle filtering,vertical distance filtering and horizontal distance filtering on the line segment features,make the length of the line feature is more uniform,which is convenient for the subsequent calculation of the camera pose.Then,in sight of the matter that the environment's illumination changes have an effect on the accuracy of the loop closure detection,the thesis proposes a loop closure detection methodology that mixes the point-line feature and therefore the illumination invariant image in the loop closure detection.Thesis build bag of visual words tree with point and line features offline firstly,and use an improved methodology for conniving the load of graphic words is projected.The assignment constant is calculated by the singular constant of the visual word,so the ultimate pictorial word is calculated by the TF-IDF.At the some time,so as to boost the hardiness of the SLAM system to the lighting surroundings,the illumination invariant image is introduced,and therefore the constraint condition of the similarity between the image and therefore the illumination invariant image is superimposed to work out whether or not a closed-loop system is generated.Finally,it absolutely tests on the it3 f database.The experimental results show that the loop closure detection rule projected during the thesis has higher accuracy and recall rate in environments with variable illumination and low-texture;Finally,a visual SLAM system for indoor mobile robots is made exploitation the relevant software system and hardware platforms,and therefore the formula during the thesis is completed by writing the corresponding formula operates package below the Golem software system(ROS).And allocated relevant experiments within the real indoor setting.The experimental results show that within the actual artificial low texture setting,the accuracy of the projected formula is higher,and once the desired target purpose is reached,the accuracy of the projected formula is higher.