Design of a perception system for the Formula Student Driverless competition: from vehicle sensorization to SLAM

openFormula Student Driverless is an international racing competition held among universities, where the vehicles must complete a set of trials without any human intervention. Together with RaceUP, the Formula Student team of the University of Padova, this thesis represents the beginning of the project to build an autonomous prototype to compete in the Driverless Cup in the 2024 season. Three important aspects of an autonomous system design will be tackled: vehicle sensorization, perception, and simultaneous localization and mapping (SLAM), with the main focus on the development of the last one. The proposed approach for the back-end is based on the optimization of a factor graph, holding information about car poses and landmarks positions, by exploiting spatial and kinematic constraints between its vertices. The full back-end pipeline has been tested thoroughly, step by step, allowing to obtain satisfactory results on the different virtual tracks used for testing. Using both modern and classical techniques, we can process information produced by the stereo camera and the LIDAR, to be able to localize the colored cones delimiting the track. The estimation of cones positions serves then as input for other important modules of the car, such as the control part and the SLAM pipeline. Finally, a complete dataset has been acquired by properly sensorizing RaceUP's last year's car: having real data represents a helpful resource to make experiments and validate the system, even without the availability of the actual vehicle prototype.Formula Student Driverless is an international racing competition held among universities, where the vehicles must complete a set of trials without any human intervention. Together with RaceUP, the Formula Student team of the University of Padova, this thesis represents the beginning of the project to build an autonomous prototype to compete in the Driverless Cup in the 2024 season. Three important aspects of an autonomous system design will be tackled: vehicle sensorization, perception, and simultaneous localization and mapping (SLAM), with the main focus on the development of the last one. The proposed approach for the back-end is based on the optimization of a factor graph, holding information about car poses and landmarks positions, by exploiting spatial and kinematic constraints between its vertices. The full back-end pipeline has been tested thoroughly, step by step, allowing to obtain satisfactory results on the different virtual tracks used for testing. Using both modern and classical techniques, we can process information produced by the stereo camera and the LIDAR, to be able to localize the colored cones delimiting the track. The estimation of cones positions serves then as input for other important modules of the car, such as the control part and the SLAM pipeline. Finally, a complete dataset has been acquired by properly sensorizing RaceUP's last year's car: having real data represents a helpful resource to make experiments and validate the system, even without the availability of the actual vehicle prototype

Extraction robuste de primitives géométriques 3D dans un nuage de points et alignement basé sur les primitives

Dans ce projet, nous étudions les problèmes de rétro-ingénierie et de contrôle de la qualité qui jouent un rôle important dans la fabrication industrielle. La rétro-ingénierie tente de reconstruire un modèle 3D à partir de nuages de points, qui s’apparente au problème de la reconstruction de la surface 3D. Le contrôle de la qualité est un processus dans lequel la qualité de tous les facteurs impliqués dans la production est abordée. En fait, les systèmes ci-dessus nécessitent beaucoup d’intervention de la part d’un utilisateur expérimenté, résultat souhaité est encore loin soit une automatisation complète du processus. Par conséquent, de nombreux défis doivent encore être abordés pour atteindre ce résultat hautement souhaitable en production automatisée. La première question abordée dans la thèse consiste à extraire les primitives géométriques 3D à partir de nuages de points. Un cadre complet pour extraire plusieurs types de primitives à partir de données 3D est proposé. En particulier, une nouvelle méthode de validation est proposée pour évaluer la qualité des primitives extraites. À la fin, toutes les primitives présentes dans le nuage de points sont extraites avec les points de données associés et leurs paramètres descriptifs. Ces résultats pourraient être utilisés dans diverses applications telles que la reconstruction de scènes on d’édifices, la géométrie constructive et etc. La seconde question traiée dans ce travail porte sur l’alignement de deux ensembles de données 3D à l’aide de primitives géométriques, qui sont considérées comme un nouveau descripteur robuste. L’idée d’utiliser les primitives pour l’alignement arrive à surmonter plusieurs défis rencontrés par les méthodes d’alignement existantes. Ce problème d’alignement est une étape essentielle dans la modélisation 3D, la mise en registre, la récupération de modèles. Enfin, nous proposons également une méthode automatique pour extraire les discontinutés à partir de données 3D d’objets manufacturés. En intégrant ces discontinutés au problème d’alignement, il est possible d’établir automatiquement les correspondances entre primitives en utilisant l’appariement de graphes relationnels avec attributs. Nous avons expérimenté tous les algorithmes proposés sur différents jeux de données synthétiques et réelles. Ces algorithmes ont non seulement réussi à accomplir leur tâches avec succès mais se sont aussi avérés supérieus aux méthodes proposées dans la literature. Les résultats présentés dans le thèse pourraient s’avérér utilises à plusieurs applications.In this research project, we address reverse engineering and quality control problems that play significant roles in industrial manufacturing. Reverse engineering attempts to rebuild a 3D model from the scanned data captured from a object, which is the problem similar to 3D surface reconstruction. Quality control is a process in which the quality of all factors involved in production is monitored and revised. In fact, the above systems currently require significant intervention from experienced users, and are thus still far from being fully automated. Therefore, many challenges still need to be addressed to achieve the desired performance for automated production. The first proposition of this thesis is to extract 3D geometric primitives from point clouds for reverse engineering and surface reconstruction. A complete framework to extract multiple types of primitives from 3D data is proposed. In particular, a novel validation method is also proposed to assess the quality of the extracted primitives. At the end, all primitives present in the point cloud are extracted with their associated data points and descriptive parameters. These results could be used in various applications such as scene and building reconstruction, constructive solid geometry, etc. The second proposition of the thesis is to align two 3D datasets using the extracted geometric primitives, which is introduced as a novel and robust descriptor. The idea of using primitives for alignment is addressed several challenges faced by existing registration methods. This alignment problem is an essential step in 3D modeling, registration and model retrieval. Finally, an automatic method to extract sharp features from 3D data of man-made objects is also proposed. By integrating the extracted sharp features into the alignment framework, it is possible implement automatic assignment of primitive correspondences using attribute relational graph matching. Each primitive is considered as a node of the graph and an attribute relational graph is created to provide a structural and relational description between primitives. We have experimented all the proposed algorithms on different synthetic and real scanned datasets. Our algorithms not only are successful in completing their tasks with good results but also outperform other methods. We believe that the contribution of them could be useful in many applications

Mathematical Explorations in Modern X-ray Crystallography

In this thesis, we explore crystallography through a mathematical lens. We review the basics of crystallography with a mathematical focus, and expand into contributions on two specific areas of the crystallographic refinement process. The first of these is the detection of twin components within crystals using the effect of twinning on detected diffraction peaks. We focus on using the information of particularly underestimated peaks along with the lattice structure to intelligently search for the most viable twin laws. The second contribution concerns the use of non-spherical form factors in crystallographic refinement, and testing of the impact of setting the form factor derivative to zero within the least-squares refinement process. We utilise numerical differentiation to approximate this derivative more exactly, and evaluate the impact of these choices for modelling the derivative through three test molecules to find that, within the current bounds of uncertainty, modelling the form factor derivative as zero has insignificant impact on the results of refinement. Additional curiosities encountered within our investigations of crystallography are also documented, such as the implementation of extinction parameters