Monocular slam for deformable scenarios.

El problema de localizar la posición de un sensor en un mapa incierto que se estima simultáneamente se conoce como Localización y Mapeo Simultáneo --SLAM--. Es un problema desafiante comparable al paradigma del huevo y la gallina. Para ubicar el sensor necesitamos conocer el mapa, pero para construir el mapa, necesitamos la posición del sensor. Cuando se utiliza un sensor visual, por ejemplo, una cámara, se denomina Visual SLAM o VSLAM. Los sensores visuales para SLAM se dividen entre los que proporcionan información de profundidad (por ejemplo, cámaras RGB-D o equipos estéreo) y los que no (por ejemplo, cámaras monoculares o cámaras de eventos). En esta tesis hemos centrado nuestra investigación en SLAM con cámaras monoculares.Debido a la falta de percepción de profundidad, el SLAM monocular es intrínsecamente más duro en comparación con el SLAM con sensores de profundidad. Los trabajos estado del arte en VSLAM monocular han asumido normalmente que la escena permanece rígida durante toda la secuencia, lo que es una suposición factible para entornos industriales y urbanos. El supuesto de rigidez aporta las restricciones suficientes al problema y permite reconstruir un mapa fiable tras procesar varias imágenes. En los últimos años, el interés por el SLAM ha llegado a las áreas médicas donde los algoritmos SLAM podrían ayudar a orientar al cirujano o localizar la posición de un robot. Sin embargo, a diferencia de los escenarios industriales o urbanos, en secuencias dentro del cuerpo, todo puede deformarse eventualmente y la suposición de rigidez acaba siendo inválida en la práctica, y por extensión, también los algoritmos de SLAM monoculares. Por lo tanto, nuestro objetivo es ampliar los límites de los algoritmos de SLAM y concebir el primer sistema SLAM monocular capaz de hacer frente a la deformación de la escena.Los sistemas de SLAM actuales calculan la posición de la cámara y la estructura del mapa en dos subprocesos concurrentes: la localización y el mapeo. La localización se encarga de procesar cada imagen para ubicar el sensor de forma continua, en cambio el mapeo se encarga de construir el mapa de la escena. Nosotros hemos adoptado esta estructura y concebimos tanto la localización deformable como el mapeo deformable ahora capaces de recuperar la escena incluso con deformación.Nuestra primera contribución es la localización deformable. La localización deformable utiliza la estructura del mapa para recuperar la pose de la cámara con una única imagen. Simultáneamente, a medida que el mapa se deforma durante la secuencia, también recupera la deformación del mapa para cada fotograma. Hemos propuesto dos familias de localización deformable. En el primer algoritmo de localización deformable, asumimos que todos los puntos están embebidos en una superficie denominada plantilla. Podemos recuperar la deformación de la superficie gracias a un modelo de deformación global que permite estimar la deformación más probable del objeto. Con nuestro segundo algoritmo de localización deformable, demostramos que es posible recuperar la deformación del mapa sin un modelo de deformación global, representando el mapa como surfels individuales. Nuestros resultados experimentales mostraron que, recuperando la deformación del mapa, ambos métodos superan tanto en robustez como en precisión a los métodos rígidos.Nuestra segunda contribución es la concepción del mapeo deformable. Es el back-end del algoritmo SLAM y procesa un lote de imágenes para recuperar la estructura del mapa para todas las imágenes y hacer crecer el mapa ensamblando las observaciones parciales del mismo. Tanto la localización deformable como el mapeo que se ejecutan en paralelo y juntos ensamblan el primer SLAM monocular deformable: \emph{DefSLAM}. Una evaluación ampliada de nuestro método demostró, tanto en secuencias controladas por laboratorio como en secuencias médicas, que nuestro método procesa con éxito secuencias en las que falla el sistema monocular SLAM actual.Nuestra tercera contribución son dos métodos para explotar la información fotométrica en SLAM monocular deformable. Por un lado, SD-DefSLAM que aprovecha el emparejamiento semi-directo para obtener un emparejamiento mucho más fiable de los puntos del mapa en las nuevas imágenes, como consecuencia, se demostró que es más robusto y estable en secuencias médicas. Por otro lado, proponemos un método de Localización Deformable Directa y Dispersa en el que usamos un error fotométrico directo para rastrear la deformación de un mapa modelado como un conjunto de surfels 3D desconectados. Podemos recuperar la deformación de múltiples superficies desconectadas, deformaciones no isométricas o superficies con una topología cambiante.<br /

The Bubble Box: Towards an Automated Visual Sensor for 3D Analysis and Characterization of Marine Gas Release Sites

Several acoustic and optical techniques have been used for characterizing natural and anthropogenic gas leaks (carbon dioxide, methane) from the ocean floor. Here, single-camera based methods for bubble stream observation have become an important tool, as they help estimating flux and bubble sizes under certain assumptions. However, they record only a projection of a bubble into the camera and therefore cannot capture the full 3D shape, which is particularly important for larger, non-spherical bubbles. The unknown distance of the bubble to the camera (making it appear larger or smaller than expected) as well as refraction at the camera interface introduce extra uncertainties. In this article, we introduce our wide baseline stereo-camera deep-sea sensor bubble box that overcomes these limitations, as it observes bubbles from two orthogonal directions using calibrated cameras. Besides the setup and the hardware of the system, we discuss appropriate calibration and the different automated processing steps deblurring, detection, tracking, and 3D fitting that are crucial to arrive at a 3D ellipsoidal shape and rise speed of each bubble. The obtained values for single bubbles can be aggregated into statistical bubble size distributions or fluxes for extrapolation based on diffusion and dissolution models and large scale acoustic surveys. We demonstrate and evaluate the wide baseline stereo measurement model using a controlled test setup with ground truth information

Structured light assisted real-time stereo photogrammetry for robotics and automation. Novel implementation of stereo matching

In this Master’s thesis project a novel implementation of a stereo matching based method is proposed. Moreover, an exhaustive analysis of the state-of-the-art algorithms in that field is outlined. Specifically, both standard and deep learning based methods have been extensively investigated, thus to provide useful insights for the designed implementation. Regarding the developed work, it is basically structured in the following manner. At first a research phase has been carried out, hence to simply and rapidly test the thought strategy. Subsequently, a first implementation of the algorithm has been designed and tested using data available from the Middlebury 2014 dataset, which is one of the most exploited dataset in the computer vision area. At this stage, numerous tests have been completed and consequently various changes to the algorithm pipeline have been made, in order to improve the final result. Finally, after that exhaustive researching phase the actual method has been designed and tested using real environment images obtained from the stereo device developed by the company, in which this work has been produced. Fundamental element of the project is indeed that stereo device. As a matter of fact, the designed algorithm in based on the data produced by the cameras that constitute it. Specifically, the main function of the system designed by LaDiMo is to make the built stereo matching based procedure simultaneously faster and accurate. As a matter of fact one of the main prerogative of the project was to create an algorithm that has to prove potential real-time results. This has been in fact, achieved by applying one of the two methods created. Specifically, it is a lightweight implementation, which strongly exploits the information coming from the LaDiMo device, thus to provide accurate results, keeping the computational time short. At the end of this Master’s thesis images showing the main outcomes obtained are proposed. Moreover, a discussion regarding the further improvements that are going to be added to the project is stated. In fact, the method implemented, being not optimized only demonstrate a potential real-time implementation, which would be certainly achieved through an efficient refactoring of the main pipeline

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Combining Features and Semantics for Low-level Computer Vision

Visual perception of depth and motion plays a significant role in understanding and navigating the environment. Reconstructing outdoor scenes in 3D and estimating the motion from video cameras are of utmost importance for applications like autonomous driving. The corresponding problems in computer vision have witnessed tremendous progress over the last decades, yet some aspects still remain challenging today. Striking examples are reflecting and textureless surfaces or large motions which cannot be easily recovered using traditional local methods. Further challenges include occlusions, large distortions and difficult lighting conditions. In this thesis, we propose to overcome these challenges by modeling non-local interactions leveraging semantics and contextual information. Firstly, for binocular stereo estimation, we propose to regularize over larger areas on the image using object-category specific disparity proposals which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The disparity proposals encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel-based graphical model and demonstrate its benefits especially in reflective regions. Secondly, for 3D reconstruction, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by localizing objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. Evaluations with respect to LIDAR ground-truth on a novel challenging suburban dataset show the advantages of modeling structural dependencies between objects. Finally, motivated by the success of deep learning techniques in matching problems, we present a method for learning context-aware features for solving optical flow using discrete optimization. Towards this goal, we present an efficient way of training a context network with a large receptive field size on top of a local network using dilated convolutions on patches. We perform feature matching by comparing each pixel in the reference image to every pixel in the target image, utilizing fast GPU matrix multiplication. The matching cost volume from the network's output forms the data term for discrete MAP inference in a pairwise Markov random field. Extensive evaluations reveal the importance of context for feature matching.Die visuelle Wahrnehmung von Tiefe und Bewegung spielt eine wichtige Rolle bei dem Verständnis und der Navigation in unserer Umwelt. Die 3D Rekonstruktion von Szenen im Freien und die Schätzung der Bewegung von Videokameras sind von größter Bedeutung für Anwendungen, wie das autonome Fahren. Die Erforschung der entsprechenden Probleme des maschinellen Sehens hat in den letzten Jahrzehnten enorme Fortschritte gemacht, jedoch bleiben einige Aspekte heute noch ungelöst. Beispiele hierfür sind reflektierende und texturlose Oberflächen oder große Bewegungen, bei denen herkömmliche lokale Methoden häufig scheitern. Weitere Herausforderungen sind niedrige Bildraten, Verdeckungen, große Verzerrungen und schwierige Lichtverhältnisse. In dieser Arbeit schlagen wir vor nicht-lokale Interaktionen zu modellieren, die semantische und kontextbezogene Informationen nutzen, um diese Herausforderungen zu meistern. Für die binokulare Stereo Schätzung schlagen wir zuallererst vor zusammenhängende Bereiche mit objektklassen-spezifischen Disparitäts Vorschlägen zu regularisieren, die wir mit inversen Grafik Techniken auf der Grundlage einer spärlichen Disparitätsschätzung und semantischen Segmentierung des Bildes erhalten. Die Disparitäts Vorschläge kodieren die Tatsache, dass die Gegenstände bestimmter Kategorien nicht willkürlich geformt sind, sondern typischerweise regelmäßige Strukturen aufweisen. Wir integrieren sie für die komplexe Objektklasse 'Auto' in Form eines nicht-lokalen Regularisierungsterm in ein Superpixel-basiertes grafisches Modell und zeigen die Vorteile vor allem in reflektierenden Bereichen. Zweitens nutzen wir für die 3D-Rekonstruktion die Tatsache, dass mit der Größe der rekonstruierten Fläche auch die Wahrscheinlichkeit steigt, Objekte von ähnlicher Art und Form in der Szene zu enthalten. Dies gilt besonders für Szenen im Freien, in denen Gebäude und Fahrzeuge oft vorkommen, die unter fehlender Textur oder Reflexionen leiden aber ähnlichkeit in der Form aufweisen. Wir nutzen diese ähnlichkeiten zur Lokalisierung von Objekten mit Detektoren und zur gemeinsamen Rekonstruktion indem ein volumetrisches Modell ihrer Form erlernt wird. Dies ermöglicht auftretendes Rauschen zu reduzieren, während fehlende Flächen vervollständigt werden, da Objekte ähnlicher Form von allen Beobachtungen der jeweiligen Kategorie profitieren. Die Evaluierung auf einem neuen, herausfordernden vorstädtischen Datensatz in Anbetracht von LIDAR-Entfernungsdaten zeigt die Vorteile der Modellierung von strukturellen Abhängigkeiten zwischen Objekten. Zuletzt, motiviert durch den Erfolg von Deep Learning Techniken bei der Mustererkennung, präsentieren wir eine Methode zum Erlernen von kontextbezogenen Merkmalen zur Lösung des optischen Flusses mittels diskreter Optimierung. Dazu stellen wir eine effiziente Methode vor um zusätzlich zu einem Lokalen Netzwerk ein Kontext-Netzwerk zu erlernen, das mit Hilfe von erweiterter Faltung auf Patches ein großes rezeptives Feld besitzt. Für das Feature Matching vergleichen wir mit schnellen GPU-Matrixmultiplikation jedes Pixel im Referenzbild mit jedem Pixel im Zielbild. Das aus dem Netzwerk resultierende Matching Kostenvolumen bildet den Datenterm für eine diskrete MAP Inferenz in einem paarweisen Markov Random Field. Eine umfangreiche Evaluierung zeigt die Relevanz des Kontextes für das Feature Matching

Individual tree measurements by means of digital aerial photogrammetry

Korpela, I. 2004. Individual tree measurements by means of digital aerial photogrammetry. Silva Fennica Monographs 3. 93 p. This study explores the plausibility of the use of multi-scale, CIR aerial photographs to conduct forest inventory at the individual tree level. Multiple digitised aerial photographs are used for manual and semi-automatic 3D positioning of tree tops, for species classification, and for measurements on tree height and crown width. A new tree top positioning algorithm is presented and tested. It incorporates template matching in a 3D search space. Also, a new method is presented for tree species classification. In it, a partition of the image space according to the continuously varying image-object-sun geometry of aerial views is performed. Discernibility of trees in aerial images is studied. The measurement accuracy and overall measurability of crown width by using manual image measurements is investigated. A simulation study is used to examine the combined effects of discernibility and photogrammetric measurement errors on stand variables. The study material contained large-scale colour and CIR image material and 7708 trees from 24 fully mappe

3D Reconstruction using Active Illumination

In this thesis we present a pipeline for 3D model acquisition. Generating 3D models of real-world objects is an important task in computer vision with many applications, such as in 3D design, archaeology, entertainment, and virtual or augmented reality. The contribution of this thesis is threefold: we propose a calibration procedure for the cameras, we describe an approach for capturing and processing photometric normals using gradient illuminations in the hardware set-up, and finally we present a multi-view photometric stereo 3D reconstruction method. In order to obtain accurate results using multi-view and photometric stereo reconstruction, the cameras are calibrated geometrically and photometrically. For acquiring data, a light stage is used. This is a hardware set-up that allows to control the illumination during acquisition. The procedure used to generate appropriate illuminations and to process the acquired data to obtain accurate photometric normals is described. The core of the pipeline is a multi-view photometric stereo reconstruction method. In this method, we first generate a sparse reconstruction using the acquired images and computed normals. In the second step, the information from the normal maps is used to obtain a dense reconstruction of an object’s surface. Finally, the reconstructed surface is filtered to remove artifacts introduced by the dense reconstruction step