Research On Indoor Three-dimensional Modeling And Target Classification For Mobile Robots

With the development of robotics technology and people's desire for better life,mobile service robots have become more prosperous.However,the main contradiction is that the autonomy and intelligence are far from people's expectation.Making mobile robots more "like people" has always been the focus for researchers.The 3D construction of mobile robots and environmental awareness technologies are becoming hotspots for the purpose of intelligence.As a key technology for mapping,SLAM has made great progress in recent years.The three-dimensional lidar sensor has accurate construction,but it is too expensive.Therefore,the 3D environment modeling method represented by visual SLAM is the key to solve perceptual problem.This paper constructs a 3D point cloud map.Firstly,the basic and critical point cloud registration algorithm in three-dimensional modeling is studied.We modify the featurebased point cloud registration algorithm for most applications in visual SLAM.A feature point quality evaluation algorithm and three-dimensional RANSAC method are proposed which improve the accuracy and robustness of the splicing point cloud process and reduce time-consuming.This paper improves ORB-SLAM2 algorithm and makes full use of the accurately estimated pose graph in realizing real-time dense mapping.All pixel points of the keyframe are reconstructed instead of merely ORB feature points.GPGPU is used to accelerate the feature extraction and matching process,which makes real-time dense mapping without increasing frame processing time.Compared with Elastic Fusion,an advanced dense SLAM algorithm,the quality of our method is obviously superior.The object autonomous positioning and segmentation algorithm is proposed for the comprehension of 3D environment.It autonomously obtains the ROI region,and searches for object position according to the proposed reference point traversal algorithm.Then object point cloud grow and segment according to voxel-based segmentation algorithm.After that,the orthogonal projection image is used as training set according to certain projection rule,which convert 3D features to 2D features.Then use transfer learning method to train projection dataset for classification.Combining class labels and position information of an object into a semantic index can construct semantic map.This paper completes the research of 3D modeling and object perception technology of mobile robot for indoor environment.Experiments show that the improved point cloud registration algorithm has higher accuracy and speed.The fast dense mapping algorithm has good point cloud quality and real-time performance.It is used to construct indoor environment map in which locates,segments,extracts features,and classifies common objects in the map.Experiments show that object location algorithm and segmentation algorithm are feasible,and the classification method based on transfer learning has high accuracy.