Dimension fitting of wheat spikes in dense 3D point clouds based on the adaptive k-means algorithm with dynamic perspectives

The use of dense 3D point clouds to obtain agricultural crop dimensions in the place of manual measurement is crucial for enabling high-throughput phenotyping. To achieve this goal, this paper proposes an adaptive k-means algorithm based on dynamic perspectives, which first performs segmentation in order to separate the wheat spikes. We also propose a method to fit the shape of each spike and measures the dimensions of each spike with the help of the Random Sample Consensus algorithm. The experimental results show that the proposed method can be applied in a complex environment where multiple wheat spikes are grown densely and that it can fit the size of most wheat spikes accurately

Automatic 3D Mapping for Tree Diameter Measurements in Inventory Operations

Forestry is a major industry in many parts of the world. It relies on forest inventory, which consists of measuring tree attributes. We propose to use 3D mapping, based on the iterative closest point algorithm, to automatically measure tree diameters in forests from mobile robot observations. While previous studies showed the potential for such technology, they lacked a rigorous analysis of diameter estimation methods in challenging forest environments. Here, we validated multiple diameter estimation methods, including two novel ones, in a new varied dataset of four different forest sites, 11 trajectories, totaling 1458 tree observations and 1.4 hectares. We provide recommendations for the deployment of mobile robots in a forestry context. We conclude that our mapping method is usable in the context of automated forest inventory, with our best method yielding a root mean square error of 3.45 cm for our whole dataset, and 2.04 cm in ideal conditions consisting of mature forest with well spaced trees

Automatic alignment of piping system components and generation of CAD models of industrial site plants

The ability to (semi-)automatically obtain CAD models from physical installations has two important benefits: (i) it can be used to identify, as soon as possible during a con struction process, any deviations from the original designs; and (ii) it can be used to document complex installations for which CAD representations are outdated or inexis tent. Both scenarios have important practical and economic value. An ongoing project in our research group aims to reconstruct CAD representations from point clouds of in dustrial sites. However, pose estimation of pipes and piping system components is not perfect, resulting in misalignments in the reconstructed scene, which is unacceptable for a CAD model. For this undergraduate thesis, I propose to use optimization techniques to fix these misalignments. I also propose to convert the detected pipes and piping system components into actual CAD model representations for a popular commercial CAD soft ware, namely AutoCAD Plant 3D