Abstract:In order to solve the problems such as manual adjustment of parameters during traditional three-dimensional (3D) point cloud reconstruction process which is time-consuming and laborious, and the registration accuracy was not guaranteed, a 3D point cloud automatic registration algorithm was proposed and applied to the 3D reconstruction research of leaf lettuce. Firstly, a Kinect camera was used to collect point cloud data from different perspectives of the leaf lettuce. Secondly, the changing patterns of parameters during the registration process were investigated through a large number of registration experiments, and accordingly each parameter’s initial value was determined due to its most positive impact to the result. Thirdly, a registration evaluation system was established, which included the inner point overlap rate, point dispersion degree and initial registration distance error, so that the automatic registration algorithm of two point clouds were implemented. Finally, based on point cloud automatic registration algorithm, a leaf lettuce point cloud 3D reconstruction was achieved because the accumulation errors were minimized through two adjacent point clouds’ automatic registration. And then the obtained point clouds were converted to the same target coordinate system therefore the leaf lettuce 3D point cloud was reconstructed. The automatic three-dimensional reconstruction experiment was carried out on 12 lettuce plants, and the results showed that under the premise, the overlap of two point clouds was not less than 30%, the automatic registration algorithm can get the optimal parameter combination by applying the registration evaluation system;the average registration error of global registration was 0.65cm, the average registration efficiency was 44.05s, and the algorithm greatly improved the accuracy and stability of registration;the leaf lettuce point cloud 3D reconstruction algorithm can effectively reduce the registration error accumulation, and provide complete structural and morphological data for further measurement of plant phenotypic parameters, and it can be used in other plants’ 3D reconstruction and phenotype researches.
