Pose-based slam with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a Mechanical Scanned Imaging Sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler Velocity Log (DVL) and a Motion Reference Unit (MRU). The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600m path within a marina environment, showing the viability of the proposed approach.Peer Reviewe

An ICP variant using a point-to-line metric

This paper describes PLICP, an ICP (iterative closest/corresponding point) variant that uses a point-to-line metric, and an exact closed-form for minimizing such metric. The resulting algorithm has some interesting properties: it converges quadratically, and in a finite number of steps. The method is validated against vanilla ICP, IDC (iterative dual correspondences), and MBICP (Metric-Based ICP) by reproducing the experiments performed in Minguez et al. (2006). The experiments suggest that PLICP is more precise, and requires less iterations. However, it is less robust to very large initial displacement errors. The last part of the paper is devoted to purely algorithmic optimization of the correspondence search; this allows for a significant speed-up of the computation. The source code is available for download

Towards autonomous localization and mapping of AUVs: a survey

Purpose The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research. Design/methodology/approach The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms. Findings As real-world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms. Research limitations/implications This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification. Practical implications The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand. Social implications There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs. Originality/value The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles

Acquisition of 3D shapes of moving objects using fringe projection profilometry

Three-dimensional (3D) shape measurement for object surface reconstruction has potential applications in many areas, such as security, manufacturing and entertainment. As an effective non-contact technique for 3D shape measurements, fringe projection profilometry (FPP) has attracted significant research interests because of its high measurement speed, high measurement accuracy and ease to implement. Conventional FPP analysis approaches are applicable to the calculation of phase differences for static objects. However, 3D shape measurement for dynamic objects remains a challenging task, although they are highly demanded in many applications. The study of this thesis work aims to enhance the measurement accuracy of the FPP techniques for the 3D shape of objects subject to movement in the 3D space. The 3D movement of objects changes not only the position of the object but also the height information with respect to the measurement system, resulting in motion-induced errors with the use of existing FPP technology. The thesis presents the work conducted for solutions of this challenging problem

다중 로봇 SLAM을 위한 상관관계 기반 지도병합 기술

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 이범희.Multi-robot simultaneous localization and mapping (SLAM) is an advanced technique used by multiple robots and autonomous vehicles to build up a collective map within an unknown environment, or to update a collective map within a known environment, while at the same time keeping track of their current location. The collective map is obtained by merging individual maps built by different multiple robots exploring the environment. When robots do not know their initial poses one another, the problem of map merging becomes challenging because the robots have different coordinate systems. If robot-to-robot measurements are not available, the problem of map merging becomes more challenging because the map transformation matrix (MTM) among robots cannot be computed directly. This dissertation presents novel map merging techniques based on the analysis of the correlation among the individual maps, which do not need the knowledge of the relative initial poses of robots and the robot-to-robot measurements. After the cross-correlation function among the spectrometric or tomographic information extracted from the individual maps is generated, the MTM is computed by taking the rotation angle and the translation amounts corresponding to the maximum cross-correlation values. The correlation-based map merging techniques with spectral information presented in this dissertation are the extensions of a conventional map merging technique. One extension is spectrum-based feature map merging (SFMM), which extracts the spectral information of feature maps from virtual supporting lines and computes the MTM by matching the extracted spectral information. The other extension is enhanced-spectrum-based map merging (ESMM), which enhances grid maps using the locations of visual objects and computes the MTM by matching the spectral information extracted from the enhanced grid maps. The two extensions overcome successfully the limitation of the conventional map merging technique. The correlation-based map merging technique with tomographic information is a new map merging technique, which is named tomographic map merging (TMM). Since tomographic analysis can provide more detailed information on grid maps according to rotation and translation than spectral analysis, the more accurate MTM can be computed by matching the tomographic information. The TMM was tested on various pairs of partial maps from real experiments in indoor and outdoor environments. The improved accuracy was verified by showing smaller map merging errors than the conventional map merging technique and several existing map merging techniques.Chapter 1 Introduction 1.1 Background and motivation 1.2 Related works 1.3 Contributions 1.4 Organization Chapter 2 Multi-Robot SLAM and Map Merging 2.1 SLAM using Particle Filters 2.2 Multi-Robot SLAM (MR-SLAM) 2.2.1 MR-SLAM with Known Initial Correspondences 2.2.2 MR-SLAM with Unknown Initial Correspondences 2.3 Map Merging Chapter 3 Map Merging based on Spectral Correlation 3.1 Spectrum-based Map Merging (SMM) 3.2 Spectrum-based Feature Map Merging (SFMM) 3.2.1 Overview of the SFMM 3.2.2 Problem Formulation for the SFMM 3.2.3 Virtual Supporting Lines (VSLs) 3.2.4 Estimation of Map Rotation with Hough Spectra 3.2.5 Rasterization of Updated Feature Maps with VSLs 3.2.6 Estimation of Map Displacements 3.3 Enhanced-Spectrum-based Map Merging (ESMM) 3.3.1 Overview of the ESMM 3.3.2 Problem Formulation for the ESMM 3.3.3 Preprocessing – Map Thinning 3.3.4 Map Enhancement 3.3.5 Estimation of Map Rotation 3.3.6 Estimation of Map Translations Chapter 4 Map Merging based on Tomographic Correlation 4.1 Overview of the TMM 4.2 Problem Formulation for the TMM 4.3 Extraction of Sinograms by the Radon Transform 4.4 Estimation of a Rotation Angle 4.5 Estimation of X-Y Translations Chapter 5 Experiments 5.1 Experimental Results of the SFMM 5.2 Experimental Results of the ESMM 5.2.1 Results in a Parking Area 5.2.2 Results in a Building Roof 5.3 Experimental Results of the TMM 5.3.1 Results in Indoor Environments 5.3.2 Results in Outdoor Environments 5.3.3 Results with a Public Dataset 5.3.4 Results of Merging More Maps 5.4 Comparison among the Proposed Techniques 5.5 Discussion Chapter 6 Conclusions BibliographyDocto

Modelling and control of an articulated underground mining vehicle

The automation of the tramming or load, haul and dump (LHD) procedure, performed by a LHD vehicle, holds the potential to improve productivity, efficiency and safety in the mining environment. Productivity is mainly increased by longer working hours; efficiency is improved by repetitive, faultless and predictable work; and safety is improved by removing the human operator from the harsh environment. However, before the automation of the process can be addressed, a thorough understanding of the process and its duty in the overall mining method is required. Therefore, the current applicable mining methods and their areas of potential automation are given. Since the automation of the LHD vehicle is at the core of this project, its implementation in the tramming process is also justified. Also, the current underground navigation methods are given and their shortcomings are named. It is concluded that infrastructure-free navigation is the only viable solution in the ever-changing mining environment. With that in mind, the feasibility of various navigation sensors is discussed and conclusions are drawn. Both kinematic and dynamic modelling of LHD vehicles are introduced. Various forms of kinematic models are given and their underlying modelling assumptions are named. The most prominent assumptions concern the vehicle’s half-length and the inclusion of a wheel-slip factor. Dynamic modelling techniques, with a strong emphasis on tyre modelling, are also stated. In order to evaluate the modelling techniques, field tests are performed on the articulated vehicles, namely the Wright 365 LHD and the Bell 1706C loader. The test on the Wright 365 LHD gives a good impression of the harsh ergonomics under which the operator has to work. A more thorough test is performed on the Bell 1706C articulated loader. The test results are then compared to simulation results obtained from the kinematic models. Also, the above-named assumptions are tested, evaluated and discussed. A dynamic model is also simulated and discussed. Lastly, two localization and control methods are given and evaluated. The first method is an open-loop nonlinear optimal control strategy with periodic position resetting and the second method is a pathtracking controller. AFRIKAANS : Automatisering van die laai-, vervoer- en dompel- (LVD) prosedure het die potensiaal om die produktiwiteit, effektiwiteit en veiligheid van die mynbedryf te verbeter. Produktiwiteit word hoofsaaklik deur langer werksure verhoog, effektiwiteit word deur herhalende, foutlose en voorspelbare werk verbeter en veiligheid word verbeter omdat menslike operateurs uit die gevaarlike ondergrondse omgewing verwyder word. Voordat aandag aan die automatisering van die prosedure geskenk kan word, moet die prosedure en die algemene mynbedrywighede rakende die prosedure deeglik bestudeer en verstaan word. As gevolg hiervan word die huidige, toepaslike mynboumetodes hier gedokumenteer. Die implementering van ʼn gekoppelde LVD-voertuig in die LVD-prosesword ook geregverdig. Verder word die huidige metodes van ondergrondse navigasie genoem en hulle tekortkominge aangedui. Die gevolgtrekking dat infrastruktuur-vrye navigasie die enigste lewensvatbare navigasiemetode in die immer veranderende ondergrondsemynbouomgewing is, word ook gemaak. In die lig daarvan word ʼn verskeidenheid sensors genoem en bespreek. Kinematiese en dinamiese modellering van ʼn LVD-voertuig word bekendgestel. Verskeie kinematiese modelle en hulle onderliggende aannames word genoem. Die mees prominente aannames is die lengte van die gekoppelde voertuig se hoofdele en die insluiting van ʼn wielglipfaktor. Die tegnieke van dinamiese modellering, met die klem op bandmodellering, word ook gegee. Praktyktoetse op gekoppelde voertuie is ook gedoen om die verskillende modelle te evalueer. Die toets op die Wright 365-LVD bied goeie insig in die strawwe ergonomiese toestande waaronder die operateurs moet werk. ʼn Deeglike toets is op ʼn BELL 1706C- gekoppelde laaier, wat kinematies identies aan ʼn LVD-voertuig is, uitgevoer. Die bevindinge van die toets word met bogenoemde modelsimulasies vergelyk en gevolgtrekkings word gemaak. Laastens word lokalisiering en beheer van ʼn LVDvoertuig behandel. Twee beheermetodes, opelus- nie-lineêre optimale beheer met periodieke herstel en padvolgingbeheer word geëvalueer en bespreek. CopyrightDissertation (MEng)--University of Pretoria, 2012.Electrical, Electronic and Computer Engineeringunrestricte

Probabilistic scan matching for motion estimation in unstructured environments

Abstract — This paper presents a probabilistic scan matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The general framework follows an iterative process of two steps: (i) computation of correspondences between scans, and (ii) estimation of the relative displacement. The contribution is a probabilistic modelling of this process that takes into account all the uncertainties involved: the uncertainty of the displacement of the sensor and the measurement noises. Furthermore, it also considers all the possible correspondences resulting from these uncertainties. This technique has been implemented and tested on a real vehicle. The experiments illustrate how the performances of this method are better than previous geometric ones in terms of robustness, accuracy and convergence. I

Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

Wo bin ich? Beiträge zum Lokalisierungsproblem mobiler Roboter

Self-localization addresses the problem of estimating the pose of mobile robots with respect to a certain coordinate system of their workspace. It is needed for various mobile robot applications like material handling in industry, disaster zone operations, vacuum cleaning, or even the exploration of foreign planets. Thus, self-localization is a very essential capability. This problem has received considerable attention over the last decades. It can be decomposed into localization on a global and local level. Global techniques are able to localize the robot without any prior knowledge about its pose with respect to an a priori known map. In contrast, local techniques aim to correct so-called odometry errors occurring during robot motion. In this thesis, the global localization problem for mobile robots is mainly addressed. The proposed method is based on matching an incremental local map to an a priori known global map. This approach is very time and memory efficient and robust to structural ambiguity as well as with respect to the occurrence of dynamic obstacles in non-static environments. The algorithm consists of several components like ego motion estimation or global point cloud matching. Nowadays most computers feature multi-core processors and thus map matching is performed by applying a parallelized variant of the Random Sample Matching (pRANSAM) approach originally devised for solving the 3D-puzzle problem. pRANSAM provides a set of hypotheses representing alleged robot poses. Techniques are discussed to postprocess the hypotheses, e.g. to decide when the robot pose is determined with a sufficient accuracy. Furthermore, runtime aspects are considered in order to facilitate localization in real-time. Finally, experimental results demonstrate the robustness of the method proposed in this thesis.Das Lokalisierungsproblem mobiler Roboter beschreibt die Aufgabe, deren Pose bezüglich eines gegebenen Weltkoordinatensystems zu bestimmen. Die Fähigkeit zur Selbstlokalisierung wird in vielen Anwendungsbereichen mobiler Roboter benötigt, wie etwa bei dem Materialtransport in der industriellen Fertigung, bei Einsätzen in Katastrophengebieten oder sogar bei der Exploration fremder Planeten. Eine Unterteilung existierender Verfahren zur Lösung des genannten Problems erfolgt je nachdem ob eine Lokalisierung auf lokaler oder auf globaler Ebene stattfindet. Globale Lokalisierungsalgorithmen bestimmen die Pose des Roboters bezüglich eines Weltkoordinatensystems ohne jegliches Vorwissen, wohingegen bei lokalen Verfahren eine grobe Schätzung der Pose vorliegt, z.B. durch gegebene Odometriedaten des Roboters. Im Rahmen dieser Dissertation wird ein neuer Ansatz zur Lösung des globalen Lokalisierungsproblems vorgestellt. Die grundlegende Idee ist, eine lokale Karte und eine globale Karte in Übereinstimmung zu bringen. Der beschriebene Ansatz ist äußerst robust sowohl gegenüber Mehrdeutigkeiten der Roboterpose als auch dem Auftreten dynamischer Hindernisse in nicht-statischen Umgebungen. Der Algorithmus besteht hauptsächlich aus drei Komponenten: Einem Scanmatcher zur Generierung der lokalen Karte, einer Methode zum matchen von lokaler und globaler Karte und einer Instanz, welche entscheidet, wann der Roboter mit hinreichender Sicherheit korrekt lokalisiert ist. Das Matching von lokaler und globaler Karte wird dabei von einer parallelisierten Variante des Random Sample Matching (pRANSAM) durchgeführt, welche eine Menge von Posenhypothesen liefert. Diese Hypothesen werden in einem weiteren Schritt analysiert, um bei hinreichender Eindeutigkeit die korrekte Roboterpose zu bestimmen. Umfangreiche Experimente belegen die Zuverlässigkeit und Genauigkeit des in dieser Dissertation vorgestellten Verfahrens