The three-dimensional modeling of plants allows not only the use of color information, as in conventional digital image processing, but also the use of geometric information for the morphological extraction of their features and the subsequent analysis of their phenotype. The generation of point clouds is one of the initial stages of this process, which is carried out in different ways. One of the techniques used for this purpose uses a rotating platform and laser sensors, which employ multiple beams of light to illuminate the measurement area and determine its depth with the principle of time of flight (ToF). However, the algorithms used to perform the three-dimensional reconstruction must be calibrated in a process that may include a large number of experiments. For this reason, artificial three-dimensional point clouds generated by simulators may be suitable, both for the validation of reconstruction algorithms on those platforms and for the analysis of plant phenotype characteristics under almost realistic conditions. Thus, with this aim, this paper describes the development of an open-source tool for the generation of artificial 3D plant point clouds, based on the simulation tool Gazebo and the Robot Operating System (ROS). This work in progress allows validating different reconstruction algorithms, as well as the characteristics of LiDAR sensors and turntables to generate 3D models in an open file format. Our source implementation is freely available online and can be obtained from https://github.com/HaroldMurcia/3D-plantModeling-with-2DLiDAR.
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