Geometric accuracy evaluation of mobile terrestrial LIDAR surveys with supporting algorithms

Mobile Mapping System (MMS) technology is widely used for many applications, hence quantifying its accuracy is a very important and essential task and is a primary focus of this research. In general, to perfrom geometric accuracy evaluation of MMS data, validation points/features are needed. A method is needed to capture a point feature off the roadway in a position where a target on the ground surface would not be visible to the scanner. In this study, eight sphere targets with 14 diameter were placed on the shoulder of the roadway over validation points on the ground. The sphere targets were constructed from injection molded spherical light fixtures. Through a calibration process, they were verified as consistent in size and shape at the 1 mm level. The targets were scanned by four different MMSs (two of design grade and two of asset grade) on two established Test Sites representing different roadway environments (highway and urban settings). Two different selectable data rates (250 KHz and 500 KHz) were also exercised in the data collection as well as two different vehicle driving techniques for data collection (with and without acceleration while the vehicle is turning). Absolute and relative accuracy of the dataset obtained from MMS are of interest. All of these characteristics and factors have been geometrically evaluated through the developed procedures. An automatic sphere target detection/estimation algorithm was developed to detect and extract the scanned sphere target points by eliminating most of the adjacent non-sphere points via a 3D Hough transform process. Following this, the sphere center is robustly located through estimation via L1-norm minimization which allows outliers (ex. tribrach points) to be detected and automatically eliminated. Subsequently the final sphere target center is estimated through least squares. This procedure is robust to several sources of non-random noise. Through error propagation, the precision of the center point estimation is SE90 = 0.20 cm (radius for spherical error, 90%). The case of disturbed targets was able to be detected with the results from this algorithm as well. Although such geometric targets have been widely used in static laser scanning, their use in Mobile Mapping has not been thoroughly studied. Another contribution from this research is that L1-estimation has been applied to all methods of forming condition equations. Those are indirect observations (line fitting), observations only (level network), and mixed model (dependent relative orientation of stereo pair images) problems. Existing published work has exclusively been applied to the indirect observations form of condition equation representation. In this test, outliers which were intentionally added to observations of all the problems were correctly detected. Additionally, L1-estimation was implemented to each of the problems by two different approaches: 1) by using a linear programming approach solved by the simplex method, 2) by a brute force method (exhaustive search for all possible sets of solutions). Results from both approaches are identical. This has verified the idea that the linear programming approach can be used as a convenient tool for implementing L1-estimation for all methods of forming the condition equations

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

Graph-based segmentation and scene understanding for context-free point clouds

The acquisition of 3D point clouds representing the surface structure of real-world scenes has become common practice in many areas including architecture, cultural heritage and urban planning. Improvements in sample acquisition rates and precision are contributing to an increase in size and quality of point cloud data. The management of these large volumes of data is quickly becoming a challenge, leading to the design of algorithms intended to analyse and decrease the complexity of this data. Point cloud segmentation algorithms partition point clouds for better management, and scene understanding algorithms identify the components of a scene in the presence of considerable clutter and noise. In many cases, segmentation algorithms operate within the remit of a specific context, wherein their effectiveness is measured. Similarly, scene understanding algorithms depend on specific scene properties and fail to identify objects in a number of situations. This work addresses this lack of generality in current segmentation and scene understanding processes, and proposes methods for point clouds acquired using diverse scanning technologies in a wide spectrum of contexts. The approach to segmentation proposed by this work partitions a point cloud with minimal information, abstracting the data into a set of connected segment primitives to support efficient manipulation. A graph-based query mechanism is used to express further relations between segments and provide the building blocks for scene understanding. The presented method for scene understanding is agnostic of scene specific context and supports both supervised and unsupervised approaches. In the former, a graph-based object descriptor is derived from a training process and used in object identification. The latter approach applies pattern matching to identify regular structures. A novel external memory algorithm based on a hybrid spatial subdivision technique is introduced to handle very large point clouds and accelerate the computation of the k-nearest neighbour function. Segmentation has been successfully applied to extract segments representing geographic landmarks and architectural features from a variety of point clouds, whereas scene understanding has been successfully applied to indoor scenes on which other methods fail. The overall results demonstrate that the context-agnostic methods presented in this work can be successfully employed to manage the complexity of ever growing repositories