Research On Simultaneous Localization And Mapping For Autonomous Interior Finishing Robot

The use of robots in the interior decoration of the house is conducive to the standardization and systemization of the construction process.With the increase of labor cost and the rapid development of robotic automation technology,more and more robots will be used in automated interior decoration.However,the application of robotics in this field is still limited,and the automation of interior decoration is far from being realized.One of the reasons for this situation is that the existing technology of SLAM(Simultaneous Localization and Mapping)is difficult to be effective in the interior decoration scene with monotonous and changing environment background,and the robot localization and environmental awareness Technology applied in the scene of automatic indoor decoration has not made a breakthrough.In this paper,we have studied a method of SLAM,which is specially used in the indoor decoration of robots,and carry out related experiments.In view of the monotonous environment background in indoor decoration scene and the change of wall visual characteristics during construction,the existing visual SLAM method applied to mobile robots has poor robustness,low accuracy and even can not be used.We propose and design a visual SLAM system for autonomous finsihing robots based on artificial projection features.The scheme adds visual features in indoor decoration environment by dot-matrix laser projector.The decoration robot uses ZED stereo camera to capture environmental information and extract visual features,match them,triangulate to build local map,and obtains the localization of the robot by tracking the local map.At the back end,the re-projection error and closure are minimized.The global pose correction of ring realizes the optimization of pose and map.Then the robot builds a dense map of the environment registering the point clouds of keyframes.Tested in our existing decoration robot,the autonomous robot has the ability of localization and mapping in the real interior decoration environment.In order to realize the automation of the interior decoration process of the finishing robot,the robot needs to obtain the position of the objects to be processed,such as cracks and defects in the wall.Therefore,we proposed and designed a visual SLAM system that integrates wall defect detection.The system uses the deep learning method to identify the wall defects from the image by training the wall inspection network,and then fuses the information of boundary range and type of the wall defects to the front-end of the SLAM,and correlate the detected defect objects according to the estimated inter-frame position and posture,and finally use the feature points in the defect boundary frame to correspond to each other.The wall defects reconstructed from map points are registered in SLAM map to establish a semantic map for automatic construction navigation of finishing robots.Through the test of the decoration robot in the real indoor decoration environment,the system realizes the function of accurately identifying the defects of the wall and registering them into the map built for the navigation of the robot,laying a foundation for the autonomous construction of the finishing robot.