Self consistent bathymetric mapping from robotic vehicles in the deep ocean

Submitted In partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and Woods Hole Oceanographic Institution June 2005Obtaining accurate and repeatable navigation for robotic vehicles in the deep ocean is difficult and consequently a limiting factor when constructing vehicle-based bathymetric maps. This thesis presents a methodology to produce self-consistent maps and simultaneously improve vehicle position estimation by exploiting accurate local navigation and utilizing terrain relative measurements. It is common for errors in the vehicle position estimate to far exceed the errors associated with the acoustic range sensor. This disparity creates inconsistency when an area is imaged multiple times and causes artifacts that distort map integrity. Our technique utilizes small terrain "submaps" that can be pairwise registered and used to additionally constrain the vehicle position estimates in accordance with actual bottom topography. A delayed state Kalman filter is used to incorporate these sub-map registrations as relative position measurements between previously visited vehicle locations. The archiving of previous positions in a filter state vector allows for continual adjustment of the sub-map locations. The terrain registration is accomplished using a two dimensional correlation and a six degree of freedom point cloud alignment method tailored for bathymetric data. The complete bathymetric map is then created from the union of all sub-maps that have been aligned in a consistent manner. Experimental results from the fully automated processing of a multibeam survey over the TAG hydrothermal structure at the Mid-Atlantic ridge are presented to validate the proposed method.This work was funded by the CenSSIS ERC of the Nation Science Foundation under grant EEC-9986821 and in part by the Woods Hole Oceanographic Institution through a grant from the Penzance Foundation

Efficient 3D Segmentation, Registration and Mapping for Mobile Robots

Sometimes simple is better! For certain situations and tasks, simple but robust methods can achieve the same or better results in the same or less time than related sophisticated approaches. In the context of robots operating in real-world environments, key challenges are perceiving objects of interest and obstacles as well as building maps of the environment and localizing therein. The goal of this thesis is to carefully analyze such problem formulations, to deduce valid assumptions and simplifications, and to develop simple solutions that are both robust and fast. All approaches make use of sensors capturing 3D information, such as consumer RGBD cameras. Comparative evaluations show the performance of the developed approaches. For identifying objects and regions of interest in manipulation tasks, a real-time object segmentation pipeline is proposed. It exploits several common assumptions of manipulation tasks such as objects being on horizontal support surfaces (and well separated). It achieves real-time performance by using particularly efficient approximations in the individual processing steps, subsampling the input data where possible, and processing only relevant subsets of the data. The resulting pipeline segments 3D input data with up to 30Hz. In order to obtain complete segmentations of the 3D input data, a second pipeline is proposed that approximates the sampled surface, smooths the underlying data, and segments the smoothed surface into coherent regions belonging to the same geometric primitive. It uses different primitive models and can reliably segment input data into planes, cylinders and spheres. A thorough comparative evaluation shows state-of-the-art performance while computing such segmentations in near real-time. The second part of the thesis addresses the registration of 3D input data, i.e., consistently aligning input captured from different view poses. Several methods are presented for different types of input data. For the particular application of mapping with micro aerial vehicles where the 3D input data is particularly sparse, a pipeline is proposed that uses the same approximate surface reconstruction to exploit the measurement topology and a surface-to-surface registration algorithm that robustly aligns the data. Optimization of the resulting graph of determined view poses then yields globally consistent 3D maps. For sequences of RGBD data this pipeline is extended to include additional subsampling steps and an initial alignment of the data in local windows in the pose graph. In both cases, comparative evaluations show a robust and fast alignment of the input data

Improving perception and locomotion capabilities of mobile robots in urban search and rescue missions

Nasazení mobilních robotů během zásahů záchranných složek je způsob, jak učinit práci záchranářů bezpečnější a efektivnější. Na roboty jsou ale při takovém použití kladeny vyšší nároky kvůli podmínkám, které při těchto událostech panují. Roboty se musejí pohybovat po nestabilních površích, ve stísněných prostorech nebo v kouři a prachu, což ztěžuje použití některých senzorů. Lokalizace, v robotice běžná úloha spočívající v určení polohy robotu vůči danému souřadnému systému, musí spolehlivě fungovat i za těchto ztížených podmínek. V této dizertační práci popisujeme vývoj lokalizačního systému pásového mobilního robotu, který je určen pro nasazení v případě zemětřesení nebo průmyslové havárie. Nejprve je předveden lokalizační systém, který vychází pouze z měření proprioceptivních senzorů a který vyvstal jako nejlepší varianta při porovnání několika možných uspořádání takového systému. Lokalizace je poté zpřesněna přidáním měření exteroceptivních senzorů, které zpomalují kumulaci nejistoty určení polohy robotu. Zvláštní pozornost je věnována možným výpadkům jednotlivých senzorických modalit, prokluzům pásů, které u tohoto typu robotů nevyhnutelně nastávají, výpočetním nárokům lokalizačního systému a rozdílným vzorkovacím frekvencím jednotlivých senzorů. Dále se věnujeme problému kinematických modelů pro přejíždění vertikálních překážek, což je další zdroj nepřesnosti při lokalizaci pásového robotu. Díky účasti na výzkumných projektech, jejichž členy byly hasičské sbory Itálie, Německa a Nizozemska, jsme měli přístup na cvičiště určená pro přípravu na zásahy během zemětřesení, průmyslových a dopravních nehod. Přesnost našeho lokalizačního systému jsme tedy testovali v podmínkách, které věrně napodobují ty skutečné. Soubory senzorických měření a referenčních poloh, které jsme vytvořili pro testování přesnosti lokalizace, jsou veřejně dostupné a považujeme je za jeden z přínosů naší práce. Tato dizertační práce má podobu souboru tří časopiseckých publikací a jednoho článku, který je v době jejího podání v recenzním řízení.eployment of mobile robots in search and rescue missions is a way to make job of human rescuers safer and more efficient. Such missions, however, require robots to be resilient to harsh conditions of natural disasters or human-inflicted accidents. They have to operate on unstable rough terrain, in confined spaces or in sensory-deprived environments filled with smoke or dust. Localization, a common task in mobile robotics which involves determining position and orientation with respect to a given coordinate frame, faces these conditions as well. In this thesis, we describe development of a localization system for tracked mobile robot intended for search and rescue missions. We present a proprioceptive 6-degrees-of-freedom localization system, which arose from the experimental comparison of several possible sensor fusion architectures. The system was modified to incorporate exteroceptive velocity measurements, which significantly improve accuracy by reducing a localization drift. A special attention was given to potential sensor outages and failures, to track slippage that inevitably occurs with this type of robots, to computational demands of the system and to different sampling rates sensory data arrive with. Additionally, we addressed the problem of kinematic models for tracked odometry on rough terrains containing vertical obstacles. Thanks to research projects the robot was designed for, we had access to training facilities used by fire brigades of Italy, Germany and Netherlands. Accuracy and robustness of proposed localization systems was tested in conditions closely resembling those seen in earthquake aftermath and industrial accidents. Datasets used to test our algorithms are publicly available and they are one of the contributions of this thesis. We form this thesis as a compilation of three published papers and one paper in review process

Perception of Unstructured Environments for Autonomous Off-Road Vehicles

Autonome Fahrzeuge benötigen die Fähigkeit zur Perzeption als eine notwendige Voraussetzung für eine kontrollierbare und sichere Interaktion, um ihre Umgebung wahrzunehmen und zu verstehen. Perzeption für strukturierte Innen- und Außenumgebungen deckt wirtschaftlich lukrative Bereiche, wie den autonomen Personentransport oder die Industrierobotik ab, während die Perzeption unstrukturierter Umgebungen im Forschungsfeld der Umgebungswahrnehmung stark unterrepräsentiert ist. Die analysierten unstrukturierten Umgebungen stellen eine besondere Herausforderung dar, da die vorhandenen, natürlichen und gewachsenen Geometrien meist keine homogene Struktur aufweisen und ähnliche Texturen sowie schwer zu trennende Objekte dominieren. Dies erschwert die Erfassung dieser Umgebungen und deren Interpretation, sodass Perzeptionsmethoden speziell für diesen Anwendungsbereich konzipiert und optimiert werden müssen. In dieser Dissertation werden neuartige und optimierte Perzeptionsmethoden für unstrukturierte Umgebungen vorgeschlagen und in einer ganzheitlichen, dreistufigen Pipeline für autonome Geländefahrzeuge kombiniert: Low-Level-, Mid-Level- und High-Level-Perzeption. Die vorgeschlagenen klassischen Methoden und maschinellen Lernmethoden (ML) zur Perzeption bzw.~Wahrnehmung ergänzen sich gegenseitig. Darüber hinaus ermöglicht die Kombination von Perzeptions- und Validierungsmethoden für jede Ebene eine zuverlässige Wahrnehmung der möglicherweise unbekannten Umgebung, wobei lose und eng gekoppelte Validierungsmethoden kombiniert werden, um eine ausreichende, aber flexible Bewertung der vorgeschlagenen Perzeptionsmethoden zu gewährleisten. Alle Methoden wurden als einzelne Module innerhalb der in dieser Arbeit vorgeschlagenen Perzeptions- und Validierungspipeline entwickelt, und ihre flexible Kombination ermöglicht verschiedene Pipelinedesigns für eine Vielzahl von Geländefahrzeugen und Anwendungsfällen je nach Bedarf. Low-Level-Perzeption gewährleistet eine eng gekoppelte Konfidenzbewertung für rohe 2D- und 3D-Sensordaten, um Sensorausfälle zu erkennen und eine ausreichende Genauigkeit der Sensordaten zu gewährleisten. Darüber hinaus werden neuartige Kalibrierungs- und Registrierungsansätze für Multisensorsysteme in der Perzeption vorgestellt, welche lediglich die Struktur der Umgebung nutzen, um die erfassten Sensordaten zu registrieren: ein halbautomatischer Registrierungsansatz zur Registrierung mehrerer 3D~Light Detection and Ranging (LiDAR) Sensoren und ein vertrauensbasiertes Framework, welches verschiedene Registrierungsmethoden kombiniert und die Registrierung verschiedener Sensoren mit unterschiedlichen Messprinzipien ermöglicht. Dabei validiert die Kombination mehrerer Registrierungsmethoden die Registrierungsergebnisse in einer eng gekoppelten Weise. Mid-Level-Perzeption ermöglicht die 3D-Rekonstruktion unstrukturierter Umgebungen mit zwei Verfahren zur Schätzung der Disparität von Stereobildern: ein klassisches, korrelationsbasiertes Verfahren für Hyperspektralbilder, welches eine begrenzte Menge an Test- und Validierungsdaten erfordert, und ein zweites Verfahren, welches die Disparität aus Graustufenbildern mit neuronalen Faltungsnetzen (CNNs) schätzt. Neuartige Disparitätsfehlermetriken und eine Evaluierungs-Toolbox für die 3D-Rekonstruktion von Stereobildern ergänzen die vorgeschlagenen Methoden zur Disparitätsschätzung aus Stereobildern und ermöglichen deren lose gekoppelte Validierung. High-Level-Perzeption konzentriert sich auf die Interpretation von einzelnen 3D-Punktwolken zur Befahrbarkeitsanalyse, Objekterkennung und Hindernisvermeidung. Eine Domänentransferanalyse für State-of-the-art-Methoden zur semantischen 3D-Segmentierung liefert Empfehlungen für eine möglichst exakte Segmentierung in neuen Zieldomänen ohne eine Generierung neuer Trainingsdaten. Der vorgestellte Trainingsansatz für 3D-Segmentierungsverfahren mit CNNs kann die benötigte Menge an Trainingsdaten weiter reduzieren. Methoden zur Erklärbarkeit künstlicher Intelligenz vor und nach der Modellierung ermöglichen eine lose gekoppelte Validierung der vorgeschlagenen High-Level-Methoden mit Datensatzbewertung und modellunabhängigen Erklärungen für CNN-Vorhersagen. Altlastensanierung und Militärlogistik sind die beiden Hauptanwendungsfälle in unstrukturierten Umgebungen, welche in dieser Arbeit behandelt werden. Diese Anwendungsszenarien zeigen auch, wie die Lücke zwischen der Entwicklung einzelner Methoden und ihrer Integration in die Verarbeitungskette für autonome Geländefahrzeuge mit Lokalisierung, Kartierung, Planung und Steuerung geschlossen werden kann. Zusammenfassend lässt sich sagen, dass die vorgeschlagene Pipeline flexible Perzeptionslösungen für autonome Geländefahrzeuge bietet und die begleitende Validierung eine exakte und vertrauenswürdige Perzeption unstrukturierter Umgebungen gewährleistet

Automated registration of unorganised point clouds from terrestrial laser scanners

Laser scanners provide a three-dimensional sampled representation of the surfaces of objects. The spatial resolution of the data is much higher than that of conventional surveying methods. The data collected from different locations of a laser scanner must be transformed into a common coordinate system. If good a priori alignment is provided and the point clouds share a large overlapping region, existing registration methods, such as the Iterative Closest Point (ICP) or Chen and Medioni’s method, work well. In practical applications of laser scanners, partially overlapping and unorganised point clouds are provided without good initial alignment. In these cases, the existing registration methods are not appropriate since it becomes very difficult to find the correspondence of the point clouds. A registration method, the Geometric Primitive ICP with the RANSAC (GPICPR), using geometric primitives, neighbourhood search, the positional uncertainty of laser scanners, and an outlier removal procedure is proposed in this thesis. The change of geometric curvature and approximate normal vector of the surface formed by a point and its neighbourhood are used for selecting the possible correspondences of point clouds. In addition, an explicit expression of the position uncertainty of measurement by laser scanners is presented in this dissertation and this position uncertainty is utilised to estimate the precision and accuracy of the estimated relative transformation parameters between point clouds. The GP-ICPR was tested with both simulated data and datasets from close range and terrestrial laser scanners in terms of its precision, accuracy, and convergence region. It was shown that the GP-ICPR improved the precision of the estimated relative transformation parameters as much as a factor of 5.In addition, the rotational convergence region of the GP-ICPR on the order of 10°, which is much larger than the ICP or its variants, provides a window of opportunity to utilise this automated registration method in practical applications such as terrestrial surveying and deformation monitoring

Simultaneous localization and mapping for inspection robots in water and sewer pipe networks: a review

At the present time, water and sewer pipe networks are predominantly inspected manually. In the near future, smart cities will perform intelligent autonomous monitoring of buried pipe networks, using teams of small robots. These robots, equipped with all necessary computational facilities and sensors (optical, acoustic, inertial, thermal, pressure and others) will be able to inspect pipes whilst navigating, selflocalising and communicating information about the pipe condition and faults such as leaks or blockages to human operators for monitoring and decision support. The predominantly manual inspection of pipe networks will be replaced with teams of autonomous inspection robots that can operate for long periods of time over a large spatial scale. Reliable autonomous navigation and reporting of faults at this scale requires effective localization and mapping, which is the estimation of the robot’s position and its surrounding environment. This survey presents an overview of state-of-the-art works on robot simultaneous localization and mapping (SLAM) with a focus on water and sewer pipe networks. It considers various aspects of the SLAM problem in pipes, from the motivation, to the water industry requirements, modern SLAM methods, map-types and sensors suited to pipes. Future challenges such as robustness for long term robot operation in pipes are discussed, including how making use of prior knowledge, e.g. geographic information systems (GIS) can be used to build map estimates, and improve the multi-robot SLAM in the pipe environmen

Representation Learning for Shape Decomposition, By Shape Decomposition

The ability to parse 3D objects into their constituent parts is essential for humans to understand and interact with the surrounding world. Imparting this skill in machines is important for various computer graphics, computer vision, and robotics tasks. Machines endowed with this skill can better interact with its surroundings, perform shape editing, texturing, recomposing, tracking, and animation. In this thesis, we ask two questions. First, how can machines decompose 3D shapes into their fundamental parts? Second, does the ability to decompose the 3D shape into these parts help learn useful 3D shape representations? In this thesis, we focus on parsing the shape into compact representations, such as parametric surface patches and Constructive Solid Geometry (CSG) primitives, which are also widely used representations in 3D modeling in computer graphics. Inspired by the advances in neural networks for 3D shape processing, we develop neural network approaches to tackle shape decomposition. First, we present CSGNet, a network architecture to parse shapes into CSG programs, which is trained using combination of supervised and reinforcement learning. Second, we present ParSeNet, a network architecture to decompose a shape into parametric surface patches (B-Spline) and geometric primitives (plane, cone, cylinder and sphere), trained on a large set of CAD models using supervised learning. The training of deep neural network architectures for 3D recognition and generation tasks requires a large amount of labeled datasets. We explore ways to alleviate this problem by relying on shape decomposition methods to guide the learning process. Towards that end, we first study the use of freely available metadata, albeit inconsistent, from shape repositories to learn 3D shape features. Later we show that learning to decompose a 3D shape into geometric primitives also helps in learning shape representations useful for semantic segmentation tasks. Finally, since most 3D shapes encountered in real life are textured, consisting of several fine-grained semantic parts, we propose a method to learn fine-grained representations for textured 3D shapes in a self-supervised manner by incorporating 3D geometric priors