Research On The Construction Method Of 3D Model Of Mobile Robot Environment Terrain Based On Binocular Stereo Vision

With the development and advancement of intelligent mobile robots,autonomous navigation technology for outdoor mobile robots has become one of the hot topics of current research.3D reconstruction of the terrain environment is a prerequisite for outdoor mobile robot navigation.3D reconstruction of the terrain environment ensures that the mobile robot perceives the surrounding terrain environment and navigates autonomously to achieve safe and stable operation.Binocular stereo vision systems have many advantages such as wide field of view,low cost and ease of operation,and are suitable for 3D reconstruction of environmental terrain.Based on the above background,this paper develops a research on 3D reconstruction technology of environmental terrain based on binocular stereo vision,the main research contents are:(1)An improved image stereo matching algorithm with Census transform and path cost is proposed.Binocular stereo matching is an important part of binocular vision 3D reconstruction,and it is particularly important to improve the quality of parallax maps.The traditional local stereo matching Census algorithm relies too much on central pixel points and has a high mis-match rate in weak texture regions,while the terrain data has fewer feature points,more weak texture regions and the original image acquired by the binocular camera is affected by noise,so an improved Census algorithm is proposed.The accuracy of the parallax map is improved by improving the selection of the central reference pixel point and the calculation of the matching cost of the traditional algorithm.The median is used instead of the central reference point,and a four-state comparison function is introduced to improve the Census transform.The cost of the improved Census transform is fused with the Absolute Differences(AD)cost,and the cross-cross construction method is used to achieve cost aggregation,and the parallax map is optimised with scan lines to improve the accuracy of the cost calculation.According to the experimental results,the false matching rate of the algorithm in this paper is reduced by 17.96%,and the matching effect has been improved to a certain extent.(2)A 3D-Scale Invariant Feature Transform(3D-SIFT)feature extraction combined with voxel filtering is proposed as a point cloud streamlining algorithm.Due to the large number of terrain point clouds,too much redundant point cloud data will cause the algorithm to slow down,while the traditional point cloud streamlining algorithm is prone to the missing feature points.The strong and weak feature points of the point cloud are extracted by 3D-SIFT,and the weak feature points are replaced by the nearest point of the centre of gravity of the voxel by the octree algorithm,and the strong feature points are merged with the weak feature points after voxel filtering to complete the point cloud streamlining.The experimental results show that the average information entropy of this algorithm reaches 3.77192,which is higher than the information entropy of other algorithms,thus proving that the algorithm in this paper retains more feature point information while streamlining the data.(3)A terrain 3D reconstruction method based on an improved point cloud alignment algorithm is proposed.Point cloud alignment is a hot topic in the field of 3D point cloud research.Point cloud alignment can transform the point cloud data of the same scene or object into the same scene to obtain the complete point cloud data,and it is especially important in terrain 3D reconstruction because of the sparsity of terrain point clouds.In order to improve the accuracy of point cloud alignment,this paper adopts the streamlining algorithm in(2)to streamline the point clouds,and uses the Sample Consensus Initial Alignment(SAC-IA)algorithm to calculate the initial matrix,and then uses the optimized Iterative Cloest Point(ICP)The algorithm is then compared with the classical point cloud alignment algorithm.The experimental results show that the algorithm improves the alignment accuracy by 99.48% and the time consumption by 28.30%,which verifies the feasibility of the algorithm.Finally,this paper combines a binocular stereo vision system to complete the reconstruction of the 3D terrain environment.The images are captured by a mobile robot-based binocular camera,binocular calibration and stereo correction are performed,the corrected images are stereo matched,point cloud data are aligned and 3D reconstruction is performed,and the reconstruction results of different reconstruction algorithms are compared and analysed.According to the experimental results,the reconstruction results of this paper can truly reflect the surface features of the terrain environment.