Abstract:Aiming at solving the location restrict of sensor and complicated calibration problem of traditional point cloud fusion and the problem of missing edges in Kinect outdoor work, a method of plant point cloud information fusion based on SICK and Kinect was put forward. The 3D accurate reconstruction of data acquired by 2D laser sensor, SICK LMS151, needed the cooperation of real speed sensor. Due to the short time of obtaining per frame data of SICK and low speed, X-axis data for each column was set the same and the distance between two columns was calculated according to the real speed and sensor working frequency. Original color point clouds of plant were merged by color images and depth images obtained by Kinect. Firstly, preprocessing was carried out to extract point cloud of plant from original point clouds, in which lots of point clouds of background and noise were involved. In order to minimize the amount of points and keep enough characteristics, voxel grid was executed to down sample the plant point cloud. Secondly, normal calculation was executed on each point of plant point cloud to compute feather information by making use of depth features and peripheral point, fast point feature histograms (FPFH) was performed to enrich the feather information, which contained 33 dimensions element for each point. Thirdly, sample consensus-initial alignment (SAC-IA) algorithm, an initial registration algorithm, was applied to register SICK laser point cloud and Kinect point cloud to provide a better spatial mapping relationship for accurate registration. Fourthly, on the basis of initial registration, the iterative closest point (ICP) algorithm was used to refine the initial transform matrix inferred by initial registration. Finally, the information fusion was adopted by ANN algorithm to find corresponding point in Kinect color point cloud from SICK point cloud. However, Kinect point cloud would lose lots of edge information when working under the sun, resulting in the fusion faults. Overlimit compensation would work, when ANN cannot find the corresponding point or the distance between corresponding point and searching point was beyond threshold, the searching point would be considered as corresponding point and found the color information by corresponding function provided by Kinect SDK. Experiments showed that the fusion method can effectively and accurately realize the information fusion between different point clouds and suppress the interference of the sun.
