Data-driven covariance estimation for the iterative closest point algorithm

Les nuages de points en trois dimensions sont un format de données très commun en robotique mobile. Ils sont souvent produits par des capteurs spécialisés de type lidar. Les nuages de points générés par ces capteurs sont utilisés dans des tâches impliquant de l’estimation d’état, telles que la cartographie ou la localisation. Les algorithmes de recalage de nuages de points, notamment l’algorithme ICP (Iterative Closest Point), nous permettent de prendre des mesures d’égo-motion nécessaires à ces tâches. La fusion des recalages dans des chaînes existantes d’estimation d’état dépend d’une évaluation précise de leur incertitude. Cependant, les méthodes existantes d’estimation de l’incertitude se prêtent mal aux données en trois dimensions. Ce mémoire vise à estimer l’incertitude de recalages 3D issus d’Iterative Closest Point (ICP). Premièrement, il pose des fondations théoriques desquelles nous pouvons articuler une estimation de la covariance. Notamment, il révise l’algorithme ICP, avec une attention spéciale sur les parties qui sont importantes pour l’estimation de la covariance. Ensuite, un article scientifique inséré présente CELLO-3D, notre algorithme d’estimation de la covariance d’ICP. L’article inséré contient une validation expérimentale complète du nouvel algorithme. Il montre que notre algorithme performe mieux que les méthodes existantes dans une grande variété d’environnements. Finalement, ce mémoire est conclu par des expérimentations supplémentaires, qui sont complémentaires à l’article.Three-dimensional point clouds are an ubiquitous data format in robotics. They are produced by specialized sensors such as lidars or depth cameras. The point clouds generated by those sensors are used for state estimation tasks like mapping and localization. Point cloud registration algorithms, such as Iterative Closest Point (ICP), allow us to make ego-motion measurements necessary to those tasks. The fusion of ICP registrations in existing state estimation frameworks relies on an accurate estimation of their uncertainty. Unfortunately, existing covariance estimation methods often scale poorly to the 3D case. This thesis aims to estimate the uncertainty of ICP registrations for 3D point clouds. First, it poses theoretical foundations from which we can articulate a covariance estimation method. It reviews the ICP algorithm, with a special focus on the parts of it that are pertinent to covariance estimation. Then, an inserted article introduces CELLO-3D, our data-driven covariance estimation method for ICP. The article contains a thorough experimental validation of the new algorithm. The latter is shown to perform better than existing covariance estimation techniques in a wide variety of environments. Finally, this thesis comprises supplementary experiments, which complement the article

Computational Analysis of Distance Operators for the Iterative Closest Point Algorithm

The Iterative Closest Point (ICP) algorithm is currently one of the most popular methods for rigid registration so that it has become the standard in the Robotics and Computer Vision communities. Many applications take advantage of it to align 2D/3D surfaces due to its popularity and simplicity. Nevertheless, some of its phases present a high computational cost thus rendering impossible some of its applications. In this work, it is proposed an efficient approach for the matching phase of the Iterative Closest Point algorithm. This stage is the main bottleneck of that method so that any efficiency improvement has a great positive impact on the performance of the algorithm. The proposal consists in using low computational cost point-to-point distance metrics instead of classic Euclidean one. The candidates analysed are the Chebyshev and Manhattan distance metrics due to their simpler formulation. The experiments carried out have validated the performance, robustness and quality of the proposal. Different experimental cases and configurations have been set up including a heterogeneous set of 3D figures, several scenarios with partial data and random noise. The results prove that an average speed up of 14% can be obtained while preserving the convergence properties of the algorithm and the quality of the final results

A Parallelized Iterative Closest Point Algorithm for Attitude Estimation

In recent decades, low-earth orbital debris has become a major concern. If the density of objects in orbit is too high, and nothing is done to remove any debris, there could be a cascade of collisions, each generating more debris, increasing the frequency of collisions even further. This would render future space missions very difficult and dangerous, if not impossible. Something must be done to remove space debris from orbit. This thesis attempts to solve one piece of the orbital debris problem – that is, tracking a piece of debris and determining its attitude and position relative to a capture vehicle. A LIDAR camera is used to acquire images of the body to be tracked. To achieve a fast and practical solution, a parallelized Iterative Closest Point (ICP) algorithm and a Kalman filter are implemented to track the attitude and position of a model rocket booster. Additionally, this thesis presents a method to increase ICP's accuracy and reliability by artificially increasing image resolution by algorithmically increasing image size. This work also explores the performance of different variations of ICP and the dependency of their performance on image size

Active SLAM for autonomous underwater exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version

Scan registration for autonomous mining vehicles using 3D-NDT

Scan registration is an essential subtask when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalization and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Strasser, which allows for accurate registration using a memory-efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory efficient scan surface representation

Assessment of 3D Facial Scan Integration in 3D Digital Workflow Using Radiographic Markers and Iterative Closest Point Algorithm

Introduction: Integration of 3 dimensional (3D) facial scanning into digital smile design workflows has been made available in multiple commercially available systems. Limited data exists on the accuracy of facial scans and accuracy of various methods of merging facial scans with cone beam computed tomography (CBCT) scans.Objective: The purpose of this prospective clinical study was to evaluate the accuracy of 2 methods used to integrate soft tissue facial scans with CBCT scans. It would allow proposal of a novel approach for integrating a 3D facial scan using facial radio-opaque markers in a 3D digital workflow.Material and methods: Fifteen CBCT and 3D face scans were obtained from patients who were undergoing treatment at MUSoD. A DICOM with RO markers and 3 STL data files from the facial scans were obtained for each patient. These files were superimposed using Exocad software. Accuracy of superimpositions was evaluated by measuring distances between RO markers on DICOM and STL data. The obtained dataset was analyzed using the paired t-test. Results: The results showed that the mean values for the 6 subsets, merging through the ICP algorithm, were 1.47-2mm. However, when merged by RO markers, the mean valuewas 0.14mm. Using a paired t-test, the novel RO points method was statistically more accurate than ICP algorithm method (