Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models

Perceptual aliasing is one of the main causes of failure for Simultaneous Localization and Mapping (SLAM) systems operating in the wild. Perceptual aliasing is the phenomenon where different places generate a similar visual (or, in general, perceptual) footprint. This causes spurious measurements to be fed to the SLAM estimator, which typically results in incorrect localization and mapping results. The problem is exacerbated by the fact that those outliers are highly correlated, in the sense that perceptual aliasing creates a large number of mutually-consistent outliers. Another issue stems from the fact that most state-of-the-art techniques rely on a given trajectory guess (e.g., from odometry) to discern between inliers and outliers and this makes the resulting pipeline brittle, since the accumulation of error may result in incorrect choices and recovery from failures is far from trivial. This work provides a unified framework to model perceptual aliasing in SLAM and provides practical algorithms that can cope with outliers without relying on any initial guess. We present two main contributions. The first is a Discrete-Continuous Graphical Model (DC-GM) for SLAM: the continuous portion of the DC-GM captures the standard SLAM problem, while the discrete portion describes the selection of the outliers and models their correlation. The second contribution is a semidefinite relaxation to perform inference in the DC-GM that returns estimates with provable sub-optimality guarantees. Experimental results on standard benchmarking datasets show that the proposed technique compares favorably with state-of-the-art methods while not relying on an initial guess for optimization.Comment: 13 pages, 14 figures, 1 tabl

Towards a robust slam framework for resilient AUV navigation

Autonomous Underwater Vehicles (AUVs) are playing an increasing part in modern navies, to the point that the control of oceans will soon be decided by their strategic use. In face of more complex missions occurring in potentially hostile environments, the resilience of such systems becomes critical. In this study, we investigate the following scenario: how does a lone AUV could recover from a temporary breakdown that has created a gap in its measurements, while remaining beneath the surface to avoid detection? It is assumed that the AUV is equipped with an active sonar and is operating in an uncharted area. The vehicle has to rely on itself by recovering its location using a Simultaneous Localization and Mapping (SLAM) algorithm. While SLAM is widely investigated and developed in the case of aerial and terrestrial robotics, the nature of the poorly structured underwater environment dramatically challenges its effectiveness. To address such a complex problem, the usual side scan sonar data association techniques are investigated under a global registration problem while applying robust graph SLAM modelling. In particular, ways to improve the global detection of features from sonar mosaic region patches that react well to the MICR similarity measure are discussed. The main contribution of this study is centered on a novel data processing framework that is able to generate different graph topologies using robust SLAM techniques. One of its advantages is to facilitate the testing of different modelling hypotheses to tackle the data gap following the temporary breakdown and make the most of the limited available information. Several research perspectives related to this framework are discussed. Notably, the possibility to further extend the proposed framework to heterogeneous datasets and the opportunity to accelerate the recovery process by inferring information about the breakdown using machine learning.PH

Development of an adaptive navigation system for indoor mobile handling and manipulation platforms

A fundamental technology enabling the autonomous behavior of mobile robotics is navigation. It is a main prerequisite for mobile robotics to fulfill high-level tasks such as handling and manipulation, and is often identified as one of the key challenges in mobile robotics. The mapping and localization as the basis for navigation are intensively researched in the last few decades. However, there are still challenges or problems needed to be solved for online operating in large-scale environments or running on low-cost and energy-saving embedded systems. In this work, new developments and usages of Light Detection And Ranging (LiDAR) based Simultaneous Localization And Mapping (SLAM) algorithms are presented. A key component of LiDAR based SLAM algorithms, the scan matching algorithm, is explored. Different scan matching algorithms are systemically experimented with different LiDARs for indoor home-like environments for the first time. The influence of properties of LiDARs in scan matching algorithms is quantitatively analyzed. Improvements to Bayes filter based and graph optimization based SLAMs are presented. The Bayes filter based SLAMs mainly use the current sensor information to find the best estimation. A new efficient implementation of Rao-Blackwellized Particle Filter based SLAM is presented. It is based on a pre-computed lookup table and the parallelization of the particle updating. The new implementation runs efficiently on recent multi-core embedded systems that fulfill low cost and energy efficiency requirements. In contrast to Bayes filter based methods, graph optimization based SLAMs utilize all the sensor information and minimize the total error in the system. A new real-time graph building model and a robust integrated Graph SLAM solution are presented. The improvements include the definition of unique direction norms for points or lines extracted from scans, an efficient loop closure detection algorithm, and a parallel and adaptive implementation. The developed algorithm outperforms the state-of-the-art algorithms in processing time and robustness especially in large-scale environments using embedded systems instead of high-end computation devices. The results of the work can be used to improve the navigation system of indoor autonomous robots, like domestic environments and intra-logistics.Eine der grundlegenden Funktionen, welche die Autonomie in der mobilen Robotik ermöglicht, ist die Navigation. Sie ist eine wesentliche Voraussetzung dafür, dass mobile Roboter selbständig anspruchsvolle Aufgaben erfüllen können. Die Umsetzung der Navigation wird dabei oft als eine der wichtigsten Herausforderungen identifiziert. Die Kartenerstellung und Lokalisierung als Grundlage für die Navigation wurde in den letzten Jahrzehnten intensiv erforscht. Es existieren jedoch immer noch eine Reihe von Problemen, z.B. die Anwendung auf große Areale oder bei der Umsetzung auf kostengünstigen und energiesparenden Embedded-Systemen. Diese Arbeit stellt neue Ansätze und Lösungen im Bereich der LiDAR-basierten simultanen Positionsbestimmung und Kartenerstellung (SLAM) vor. Eine Schlüsselkomponente der LiDAR-basierten SLAM, die so genannten Scan-Matching-Algorithmen, wird näher untersucht. Verschiedene Scan-Matching-Algorithmen werden zum ersten Mal systematisch mit verschiedenen LiDARs für den Innenbereich getestet. Der Einfluss von LiDARs auf die Eigenschaften der Algorithmen wird quantitativ analysiert. Verbesserungen an Bayes-filterbasierten und graphoptimierten SLAMs werden in dieser Arbeit vorgestellt. Bayes-filterbasierte SLAMs verwenden hauptsächlich die aktuellen Sensorinformationen, um die beste Schätzung zu finden. Eine neue effiziente Implementierung des auf Partikel-Filter basierenden SLAM unter der Verwendung einer Lookup-Tabelle und der Parallelisierung wird vorgestellt. Die neue Implementierung kann effizient auf aktuellen Embedded-Systemen laufen. Im Gegensatz dazu verwenden Graph-SLAMs alle Sensorinformationen und minimieren den Gesamtfehler im System. Ein neues Echtzeitmodel für die Grafenerstellung und eine robuste integrierte SLAM-Lösung werden vorgestellt. Die Verbesserungen umfassen die Definition von eindeutigen Richtungsnormen für Scan, effiziente Algorithmen zur Erkennung von Loop Closures und eine parallele und adaptive Implementierung. Der entwickelte und auf eingebetteten Systemen eingesetzte Algorithmus übertrifft die aktuellen Algorithmen in Geschwindigkeit und Robustheit, insbesondere für große Areale. Die Ergebnisse der Arbeit können für die Verbesserung der Navigation von autonomen Robotern im Innenbereich, häuslichen Umfeld sowie der Intra-Logistik genutzt werden

Hierarchische Modelle für das visuelle Erkennen und Lernen von Objekten, Szenen und Aktivitäten

In many computer vision applications, objects have to be learned and recognized in images or image sequences. Most of these objects have a hierarchical structure.For example, 3d objects can be decomposed into object parts, and object parts, in turn, into geometric primitives. Furthermore, scenes are composed of objects. And also activities or behaviors can be divided hierarchically into actions, these into individual movements, etc. Hierarchical models are therefore ideally suited for the representation of a wide range of objects used in applications such as object recognition, human pose estimation, or activity recognition. In this work new probabilistic hierarchical models are presented that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects, object parts or actions and movements in order to share calculations and avoid redundant information. We will introduce online and offline learning methods, which enable to create efficient hierarchies based on small or large training datasets, in which poses or articulated structures are given by instances. Furthermore, we present inference approaches for fast and robust detection. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. They will be used in an unified hierarchical framework spatially for object recognition as well as spatiotemporally for activity recognition. The unified generic hierarchical framework allows us to apply the proposed models in different projects. Besides classical object recognition it is used for detection of human poses in a project for gait analysis. The activity detection is used in a project for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.In zahlreichen Computer Vision Anwendungen müssen Objekte in einzelnen Bildern oder Bildsequenzen erlernt und erkannt werden. Viele dieser Objekte sind hierarchisch aufgebaut.So lassen sich 3d Objekte in Objektteile zerlegen und Objektteile wiederum in geometrische Grundkörper. Und auch Aktivitäten oder Verhaltensmuster lassen sich hierarchisch in einzelne Aktionen aufteilen, diese wiederum in einzelne Bewegungen usw. Für die Repräsentation sind hierarchische Modelle dementsprechend gut geeignet. In dieser Arbeit werden neue probabilistische hierarchische Modelle vorgestellt, die es ermöglichen auch mehrere Objekte verschiedener Kategorien, Skalierungen, Rotationen und aus verschiedenen Blickrichtungen effizient zu repräsentieren. Eine Idee ist hierbei, Ähnlichkeiten unter Objekten, Objektteilen oder auch Aktionen und Bewegungen zu nutzen, um redundante Informationen und Mehrfachberechnungen zu vermeiden. In der Arbeit werden online und offline Lernverfahren vorgestellt, die es ermöglichen, effiziente Hierarchien auf Basis von kleinen oder großen Trainingsdatensätzen zu erstellen, in denen Posen und bewegliche Strukturen durch Beispiele gegeben sind. Des Weiteren werden Inferenzansätze zur schnellen und robusten Detektion vorgestellt. Diese werden innerhalb eines einheitlichen hierarchischen Frameworks sowohl räumlich zur Objekterkennung als auch raumzeitlich zur Aktivitätenerkennung verwendet. Das einheitliche Framework ermöglicht die Anwendung des vorgestellten Modells innerhalb verschiedener Projekte. Neben der klassischen Objekterkennung wird es zur Erkennung von menschlichen Posen in einem Projekt zur Ganganalyse verwendet. Die Aktivitätenerkennung wird in einem Projekt zur Gestaltung altersgerechter Lebenswelten genutzt, um in intelligenten Wohnräumen Aktivitäten und Verhaltensmuster von Bewohnern zu erkennen. Im Rahmen eines Projektes zur Parklückenvermessung mithilfe eines intelligenten Fahrzeuges werden die vorgestellten Ansätze verwendet, um das Umfeld des Fahrzeuges hierarchisch zu modellieren und dadurch das Szenenverstehen zu ermöglichen

Estimating and understanding motion : from diagnostic to robotic surgery

Estimating and understanding motion from an image sequence is a central topic in computer vision. The high interest in this topic is because we are living in a world where many events that occur in the environment are dynamic. This makes motion estimation and understanding a natural component and a key factor in a widespread of applications including object recognition , 3D shape reconstruction, autonomous navigation and medica! diagnosis. Particularly, we focus on the medical domain in which understanding the human body for clinical purposes requires retrieving the organs' complex motion patterns, which is in general a hard problem when using only image data. In this thesis, we cope with this problem by posing the question - How to achieve a realistic motion estimation to offer a better clinical understanding? We focus this thesis on answering this question by using a variational formulation as a basis to understand one of the most complex motions in the human's body, the heart motion, through three different applications: (i) cardiac motion estimation for diagnostic, (ii) force estimation and (iii) motion prediction, both for robotic surgery. Firstly, we focus on a central topic in cardiac imaging that is the estimation of the cardiac motion. The main aim is to offer objective and understandable measures to physicians for helping them in the diagnostic of cardiovascular diseases. We employ ultrafast ultrasound data and tools for imaging motion drawn from diverse areas such as low-rank analysis and variational deformation to perform a realistic cardiac motion estimation. The significance is that by taking low-rank data with carefully chosen penalization, synergies in this complex variational problem can be created. We demonstrate how our proposed solution deals with complex deformations through careful numerical experiments using realistic and simulated data. We then move from diagnostic to robotic surgeries where surgeons perform delicate procedures remotely through robotic manipulators without directly interacting with the patients. As a result, they lack force feedback, which is an important primary sense for increasing surgeon-patient transparency and avoiding injuries and high mental workload. To solve this problem, we follow the conservation principies of continuum mechanics in which it is clear that the change in shape of an elastic object is directly proportional to the force applied. Thus, we create a variational framework to acquire the deformation that the tissues undergo due to an applied force. Then, this information is used in a learning system to find the nonlinear relationship between the given data and the applied force. We carried out experiments with in-vivo and ex-vivo data and combined statistical, graphical and perceptual analyses to demonstrate the strength of our solution. Finally, we explore robotic cardiac surgery, which allows carrying out complex procedures including Off-Pump Coronary Artery Bypass Grafting (OPCABG). This procedure avoids the associated complications of using Cardiopulmonary Bypass (CPB) since the heart is not arrested while performing the surgery on a beating heart. Thus, surgeons have to deal with a dynamic target that compromisetheir dexterity and the surgery's precision. To compensate the heart motion, we propase a solution composed of three elements: an energy function to estimate the 3D heart motion, a specular highlight detection strategy and a prediction approach for increasing the robustness of the solution. We conduct evaluation of our solution using phantom and realistic datasets. We conclude the thesis by reporting our findings on these three applications and highlight the dependency between motion estimation and motion understanding at any dynamic event, particularly in clinical scenarios.L’estimació i comprensió del moviment dins d’una seqüència d’imatges és un tema central en la visió per ordinador, el que genera un gran interès perquè vivim en un entorn ple d’esdeveniments dinàmics. Per aquest motiu és considerat com un component natural i factor clau dins d’un ampli ventall d’aplicacions, el qual inclou el reconeixement d’objectes, la reconstrucció de formes tridimensionals, la navegació autònoma i el diagnòstic de malalties. En particular, ens situem en l’àmbit mèdic en el qual la comprensió del cos humà, amb finalitats clíniques, requereix l’obtenció de patrons complexos de moviment dels òrgans. Aquesta és, en general, una tasca difícil quan s’utilitzen només dades de tipus visual. En aquesta tesi afrontem el problema plantejant-nos la pregunta - Com es pot aconseguir una estimació realista del moviment amb l’objectiu d’oferir una millor comprensió clínica? La tesi se centra en la resposta mitjançant l’ús d’una formulació variacional com a base per entendre un dels moviments més complexos del cos humà, el del cor, a través de tres aplicacions: (i) estimació del moviment cardíac per al diagnòstic, (ii) estimació de forces i (iii) predicció del moviment, orientant-se les dues últimes en cirurgia robòtica. En primer lloc, ens centrem en un tema principal en la imatge cardíaca, que és l’estimació del moviment cardíac. L’objectiu principal és oferir als metges mesures objectives i comprensibles per ajudar-los en el diagnòstic de les malalties cardiovasculars. Fem servir dades d’ultrasons ultraràpids i eines per al moviment d’imatges procedents de diverses àrees, com ara l’anàlisi de baix rang i la deformació variacional, per fer una estimació realista del moviment cardíac. La importància rau en que, en prendre les dades de baix rang amb una penalització acurada, es poden crear sinergies en aquest problema variacional complex. Mitjançant acurats experiments numèrics, amb dades realístiques i simulades, hem demostrat com les nostres propostes solucionen deformacions complexes. Després passem del diagnòstic a la cirurgia robòtica, on els cirurgians realitzen procediments delicats remotament, a través de manipuladors robòtics, sense interactuar directament amb els pacients. Com a conseqüència, no tenen la percepció de la força com a resposta, que és un sentit primari important per augmentar la transparència entre el cirurgià i el pacient, per evitar lesions i per reduir la càrrega de treball mental. Resolem aquest problema seguint els principis de conservació de la mecànica del medi continu, en els quals està clar que el canvi en la forma d’un objecte elàstic és directament proporcional a la força aplicada. Per això hem creat un marc variacional que adquireix la deformació que pateixen els teixits per l’aplicació d’una força. Aquesta informació s’utilitza en un sistema d’aprenentatge, per trobar la relació no lineal entre les dades donades i la força aplicada. Hem dut a terme experiments amb dades in-vivo i ex-vivo i hem combinat l’anàlisi estadístic, gràfic i de percepció que demostren la robustesa de la nostra solució. Finalment, explorem la cirurgia cardíaca robòtica, la qual cosa permet realitzar procediments complexos, incloent la cirurgia coronària sense bomba (off-pump coronary artery bypass grafting o OPCAB). Aquest procediment evita les complicacions associades a l’ús de circulació extracorpòria (Cardiopulmonary Bypass o CPB), ja que el cor no s’atura mentre es realitza la cirurgia. Això comporta que els cirurgians han de tractar amb un objectiu dinàmic que compromet la seva destresa i la precisió de la cirurgia. Per compensar el moviment del cor, proposem una solució composta de tres elements: un funcional d’energia per estimar el moviment tridimensional del cor, una estratègia de detecció de les reflexions especulars i una aproximació basada en mètodes de predicció, per tal d’augmentar la robustesa de la solució. L’avaluació de la nostra solució s’ha dut a terme mitjançant conjunts de dades sintètiques i realistes. La tesi conclou informant dels nostres resultats en aquestes tres aplicacions i posant de relleu la dependència entre l’estimació i la comprensió del moviment en qualsevol esdeveniment dinàmic, especialment en escenaris clínics.Postprint (published version

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance