Research And Implementation Of Point Cloud Map Fusion Algorithm Based On Feature Extraction

With the extensive application of 3D stereo equipment and intelligent robots in scientific research and real life,3D point cloud data has become a common data format in computer vision.And it has played an significant role in map-building,tracking and navigation.When a robot is building a map in a wide range of scene or complicated indoor environment,if using a single robot to complete the task,the efficiency will be reduced.At this point,we need a number of robots to work together and align these maps.Point cloud fusion uses a rigid body transformation to fuse different maps obtained from different angles and different positions.Point cloud fusion has become a core work in 3D reconstruction,intelligent navigation,scene recognition and so on.Besides the relevant production makes map fusion full of commercial significance in real life.Based on thoroughly analyzing the initial and accurate stage of point cloud map fusion,this paper proposed initial point cloud map fusion algorithm based on feature extraction on 3DSIFT keypoints and multi-resolution point cloud approximation nearest point accurate fusion algorithm.The main work and achievements are as follows:Firstly,in order to overcome the problem of the existing methods can not adapt to viewpoint change and cast much time extracting geometric features from point cloud map which is full of a large number of three-dimensional points,the paper extends the SIFT operator of 2D images to 3D point cloud maps.We construct scale space in point cloud and build difference of Gaussian space by subtracting adjacent layers in scale space.In difference of Gaussian space,extreme points can be computed.To determine the direction of keypoints,we calculate the azimuth and elevation of extreme points' neighbors.The 3D-SIFT descriptor can be computed by angle division.Secondly,the FPFH feature extraction algorithm exists the problem of weight coefficients overflow and the statistic interval is not accurate enough which causes a lot of feature matching errors.So,we propose IPFH using the exponential function to re-calculate the weight coefficient and segmenting the statistical interval to improve the accuracy.What's more,for the traditional ICP accurate fusion algorithm which has the problem of too high time consumption and low efficiency,we optimize the algorithm using the following strategies: we use the method based on octree to sample the point cloud under different degrees producing several point clouds with different resolutions.Different resolutions of point cloud maps use different iterations while processing And we also narrow search range to overlapping region.After that our method improves the efficiency of the original ICP algorithm greatly.At last,we use standard dataset and point cloud dataset obtained by kinect from the scene of our lab to test our methods in different stage including the comparison of FPFH and IPFH,our initial map fusion method and the original,our accurate point cloud fusion method and traditional ICP.We evaluate the results using table and pictures.The experimental results show that the algorithm proposed in this paper has greatly improved the efficiency and accuracy compared with the original algorithm,which proves the effectiveness of the proposed algorithm.