Positional estimation techniques for an autonomous mobile robot

Techniques for positional estimation of a mobile robot navigation in an indoor environment are described. A comprehensive review of the various positional estimation techniques studied in the literature is first presented. The techniques are divided into four different types and each of them is discussed briefly. Two different kinds of environments are considered for positional estimation; mountainous natural terrain and an urban, man-made environment with polyhedral buildings. In both cases, the robot is assumed to be equipped with single visual camera that can be panned and tilted and also a 3-D description (world model) of the environment is given. Such a description could be obtained from a stereo pair of aerial images or from the architectural plans of the buildings. Techniques for positional estimation using the camera input and the world model are presented

Simultaneous localization and map-building using active vision

An active approach to sensing can provide the focused measurement capability over a wide field of view which allows correctly formulated Simultaneous Localization and Map-Building (SLAM) to be implemented with vision, permitting repeatable long-term localization using only naturally occurring, automatically-detected features. In this paper, we present the first example of a general system for autonomous localization using active vision, enabled here by a high-performance stereo head, addressing such issues as uncertainty-based measurement selection, automatic map-maintenance, and goal-directed steering. We present varied real-time experiments in a complex environment.Published versio

Istraživanje i modeliranje nepoznatog poligonalnog prostora zasnovano na nesigurnim podacima udaljenosti

We consider problem of exploration and mapping of unknown indoor environments using laser range finder. We assume a setup with a resolved localization problem and known uncertainty sensor models. Most exploration algorithms are based on detection of a boundary between explored and unexplored regions. They are, however, not efficient in practice due to uncertainties in measurement, localization and map building. The exploration and mapping algorithm is proposed that extends Ekman’s exploration algorithm by removing rigid constraints on the range sensor and robot localization. The proposed algorithm includes line extraction algorithm developed by Pfister, which incorporates noise models of the range sensor and robot’s pose uncertainty. A line representation of the range data is used for creating polygon that represents explored region from each measurement pose. The polygon edges that do not correspond to real environmental features are candidates for a new measurement pose. A general polygon clipping algorithm is used to obtain the total explored region as the union of polygons from different measurement poses. The proposed algorithm is tested and compared to the Ekman’s algorithm by simulations and experimentally on a Pioneer 3DX mobile robot equipped with SICK LMS-200 laser range finder.Razmatramo problem istraživanja i izgradnje karte nepoznatog unutarnjeg prostora koristeći laserski sensor udaljenosti. Pretpostavljamo riješenu lokalizaciju robota i poznati model nesigurnosti senzora. Većna se algoritama istraživanja zasniva na otkrivanju granica istraženog i neistraženog područja. Međutim, u praksi nisu učinkoviti zbog nesigurnosti mjerenja, lokalizacije i izgradnje karte. Razvijen je algoritam istraživanja i izgradnje karte koji proširuje Ekmanov algoritam uklanjanjem strogih ograničenja na senzor udaljenosti i lokalizaciju robota. Razvijeni algoritam uključuje algoritam izdvajanja linijskih segmenata prema Pfisteru, koji uzima u obzir utjecaje zašumljenosti senzora i nesigurnosti položaja mobilnog robota. Linijska reprezentacija podataka udaljenosti koristi se za stvaranje poligona koji predstavlja istraženo područje iz svakog mjernog položaja. Bridovi poligona koji se ne podudaraju sa stvarnim značajkama prostora su kandidati za novi mjerni položaj. Algoritam općenitog isijecanja poligona korišten je za dobivanje ukupnog istraženog područja kao unija poligona iz različitih mjernih položaja. Razvijeni algoritam testiran je i uspoređen s izvornim Ekmanovim algoritmom simulacijski i eksperimentalno na mobilnom robotu Pioneer 3DX opremljenim laserskim senzorom udaljenosti SICK LMS-200

F-formation Detection: Individuating Free-standing Conversational Groups in Images

Detection of groups of interacting people is a very interesting and useful task in many modern technologies, with application fields spanning from video-surveillance to social robotics. In this paper we first furnish a rigorous definition of group considering the background of the social sciences: this allows us to specify many kinds of group, so far neglected in the Computer Vision literature. On top of this taxonomy, we present a detailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detection of groups in still images, which is based on a graph-cuts framework for clustering individuals; in particular we are able to codify in a computational sense the sociological definition of F-formation, that is very useful to encode a group having only proxemic information: position and orientation of people. We call the proposed method Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all the state of the art methods in terms of different accuracy measures (some of them are brand new), demonstrating also a strong robustness to noise and versatility in recognizing groups of various cardinality.Comment: 32 pages, submitted to PLOS On

View-Invariant Regions and Mobile Robot Self-Localization

This paper addresses the problem of mobile robot self-localization given a polygonal map and a set of observed edge segments. The standard approach to this problem uses interpretation tree search with pruning heuristics to match observed edges to map edges. Our approach introduces a preprocessing step in which the map is decomposed into 'view-invariant regions' (VIRs). The VIR decomposition captures information about map edge visibility, and can be used for a variety of robot navigation tasks. Basing self-localization search on VIRs greatly reduces the branching factor of the search tree and thereby simplifies the search task. In this paper we define the VIR decomposition and give algorithms for its computation and for self-localization search. We present results of simulations comparing standard and VIR-based search, and discuss the application of the VIR decomposition to other problems in robot navigation

Vision based localization of mobile robots

Mobile robotics is an active and exciting sub-field of Computer Science. Its importance is easily witnessed in a variety of undertakings from DARPA\u27s Grand Challenge to NASA\u27s Mars exploration program. The field is relatively young, and still many challenges face roboticists across the board. One important area of research is localization, which concerns itself with granting a robot the ability to discover and continually update an internal representation of its position. Vision based sensor systems have been investigated [8,22,27], but to much lesser extent than other popular techniques [4,6,7,9,10]. A custom mobile platform has been constructed on top of which a monocular vision based localization system has been implemented. The rigorous gathering of empirical data across a large group of parameters germane to the problem has led to various findings about monocular vision based localization and the fitness of the custom robot platform. The localization component is based on a probabilistic technique called Monte-Carlo Localization (MCL) that tolerates a variety of different sensors and effectors, and has further proven to be adept at localization in diverse circumstances. Both a motion model and sensor model that drive the particle filter at the algorithm\u27s core have been carefully derived. The sensor model employs a simple correlation process that leverages color histograms and edge detection to filter robot pose estimations via the on board vision. This algorithm relies on image matching to tune position estimates based on a priori knowledge of its environment in the form of a feature library. It is believed that leveraging different computationally inexpensive features can lead to efficient and robust localization with MCL. The central goal of this thesis is to implement and arrive at such a conclusion through the gathering of empirical data. Section 1 presents a brief introduction to mobile robot localization and robot architectures, while section 2 covers MCL itself in more depth. Section 3 elaborates on the localization strategy, modeling and implementation that forms the basis of the trials that are presented toward the end of that section. Section 4 presents a revised implementation that attempts to address shortcomings identified during localization trials. Finally in section 5, conclusions are drawn about the effectiveness of the localization implementation and a path to improved localization with monocular vision is posited

InLoc: Indoor Visual Localization with Dense Matching and View Synthesis

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor environments. The method proceeds along three steps: (i) efficient retrieval of candidate poses that ensures scalability to large-scale environments, (ii) pose estimation using dense matching rather than local features to deal with textureless indoor scenes, and (iii) pose verification by virtual view synthesis to cope with significant changes in viewpoint, scene layout, and occluders. Second, we collect a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data

Robust range-based localization and motion planning under uncertainty using ultra-wideband radio

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 145-149).The work presented in this thesis addresses two problems: accurately localizing a mobile robot using ultra-wideband (UWB) radio signals in GPS-denied environments; and planning robot trajectories that incorporate belief uncertainty using probabilistic state estimates. Addressing the former, we improve upon traditional approaches to range-based localization by capturing non-linear sensor dynamics using a Monte Carlo method for hidden bias estimation. For the latter, we overcome current limitations of scalable belief space planning by adapting the Probabilistic Roadmap algorithm to enable trajectory search in belief space for minimal uncertainty paths. We contribute a novel solution motivated by linear least-squares estimation and the Riccati equation that provides linear belief updates, allowing us to combine several prediction and measurement steps into one efficient update. This reduces the time required to compute a plan by over two orders of magnitude, leading to a tractable belief space planning method which we call the Belief Roadmap (BRM) algorithm.by Samuel J. Prentice.M.Eng

Evaluation of automated decisionmaking methodologies and development of an integrated robotic system simulation

A generic computer simulation for manipulator systems (ROBSIM) was implemented and the specific technologies necessary to increase the role of automation in various missions were developed. The specific items developed are: (1) capability for definition of a manipulator system consisting of multiple arms, load objects, and an environment; (2) capability for kinematic analysis, requirements analysis, and response simulation of manipulator motion; (3) postprocessing options such as graphic replay of simulated motion and manipulator parameter plotting; (4) investigation and simulation of various control methods including manual force/torque and active compliances control; (5) evaluation and implementation of three obstacle avoidance methods; (6) video simulation and edge detection; and (7) software simulation validation

Analysis and mitigation of site-dependent effects in static and kinematic GNSS applications

Satellitensignale unterliegen auf ihrem Weg von der Satelliten- zur Empfangsantenne einer Vielzahl an Einflüssen die zu Abweichungen führen. Heutzutage stellen in vielen Anwendungsbereichen insbesondere die stationsspezifischen Anteile, welche sich in Mehrwegeeffekte aus dem Fernfeld, NLOS-Empfang und Signalbeugung, den Einfluss der Satellitengeometrie und Antennennahfeldeffekte untergliedern lassen, einen der genauigkeitsbegrenzenden Faktoren in der satellitengestützten Positionsbestimmung dar. Dies ist dadurch begründet, dass durch die Abhängigkeit von der individuell vorliegenden Antennenumgebung eine Minimierung der Einflüsse erheblich erschwert wird und etablierte Strategien, wie beispielsweise die Differenzbildung in relativen Positionierungsansätzen, in der Regel nicht anwendbar sind. Obwohl diese Effekte bereits seit den frühesten Entwicklungen auf dem Gebiet der satellitengestützten Positionsbestimmung untersucht wurden, ist eine vollumfängliche Lösungsstrategie auch in der heutigen Zeit noch nicht verfügbar. Daher hat diese Thematik nicht an Relevanz verloren und es besteht noch immer der Bedarf an weiteren Untersuchungen zur Vertiefung des Verständnisses und zur Erweiterung des Portfolios an verfügbaren Minimierungsansätzen. In dieser Arbeit werden die vier unterschiedlichen Effekte vor dem Hintergrund der hochpräzisen Positionsbestimmung in statischen und kinematischen GNSS-Anwendungen adressiert. Der wesentliche Fokus der Untersuchungen liegt hierbei auf der Detektion und Elimination betroffener Satellitensignale durch die Einbindung detaillierter Umgebungsmodelle aus terrestrischen Messverfahren. Auf Basis dieser methodischen und empirischen Analysen lassen sich für die einzelnen Effekte vier Hauptaspekte herausstellen: (1) Da Antennennahfeldeffekte primär den Messsensor selbst beeinflussen und folglich die angestrebte Detektion und Elimination zur Minimierung nicht geeignet ist, wird alternativ die Minimierung des Einflusses durch spezielle Antennenaufbauten empirisch analysiert. Daraus resultierend werden mit exakt identischen Antennenaufbauten erreichbare Genauigkeiten im Submillimeterbereich nachgewiesen. (2) Der Einfluss auf die Positionsgenauigkeit der potentiell durch eine Signalelimination hervorgerufenen Verschlechterung der Satellitengeometrie kann durch Simulationen generischer Abschattungsszenarien als unkritisch identifiziert werden. Darüber hinaus wird eine Methode zur Integration der Qualität der Satellitengeometrie in die Wegpunktplanung von UAVs entwickelt, welche sowohl in der Planungsphase, als auch während des UAV-Fluges eine Anpassung und Optimierung der Flugroute ermöglicht. (3) Auf Basis mittels terrestrischer Laserscanner erzeugter Punktwolken wird eine Methode zur Erzeugung von Elevationsmasken entwickelt, welche adaptiv gegenüber der vorliegenden Antennenumgebung sind und eine effektive Detektion und Elimination von Satellitensignalen erlauben, die NLOS-Empfang oder Signalbeugung unterliegen. Diese Minimierungsstrategie ist sowohl in statischen, als auch kinematischen Anwendungen einsetzbar und ermöglicht bei zusätzlicher Einbindung von Fresnel Zonen auch die Berücksichtigung der Ausbreitungseigenschaften elektromagnetischer Wellen. (4) Als vorbereitender Schritt für die Entwicklung von Methoden zur Detektion und Eliminierung von Fernfeld-Mehrwegeeffekten werden die Voraussetzungen für die Entstehung der Effekte untersucht. Durch Vergleich simulierter und beobachteter SNR-Zeitreihen und der Berücksichtigung von Fresnel Zonen kann eine Überlappung von 50% zwischen Fresnel-Zone und Reflektorfläche als bereits ausreichend für eine potentielle Mehrwegebelastung identifiziert werden. In der Gesamtbetrachtung liefern die in dieser Arbeit gewonnenen Erkenntnisse und entwickelten Methoden einen relevanten Beitrag zu dem übergeordneten Ziel einer ganzheitlichen Minimierung stationsspezifischer Abweichungen und ermöglichen so eine signifikante Verbesserung der Positionsgenauigkeit unter schwierigen GNSS-Bedingungen. Darüber hinaus nimmt diese Arbeit den in den letzten Jahren forcierten Trend von einer punktweisen zu einer flächenhaften Objekterfassung an, indem das Potenzial einer detaillierten und effizienten Erfassung der Antennenumgebung mittels terrestrischer Laserscanner zur Minimierung und Analyse stationsspezifischer Abweichungen bei der satellitengestützten Positionsbestimmung aufzeigt und genutzt wird.Satellite signals are subject to various error sources on their way from the satellite to the receiving antenna. Nowadays, in many fields of application, the site-dependent parts, which can be separated into far-field multipath, NLOS reception and signal diffraction, the influence of the satellite geometry and antenna near-field effects, are one of the accuracy limiting factors in satellite-based positioning. This is due to the fact that the dependence on the individual antenna environment considerably impedes a minimization of the influences and established strategies, such as double-differencing in relative positioning approaches, are generally not applicable. Although these effects have been subject to scientific research since the earliest developments in the field of satellite-based positioning, an all-embracing solution is still lacking. Therefore, this topic has not lost its relevance and there is still a need for further investigations to deepen the understanding and expanding the portfolio of available mitigation techniques. In this dissertation, the four different effects are addressed against the background of high-precision static and kinematic GNSS applications. In this context, the main focus of the investigations is on the detection and exclusion of affected satellite signals, by integrating detailed environment models derived from terrestrial measurements. Based on these methodological and empirical analyses, four main aspects can be highlighted for the different effects: (1) Since antenna near-field effects primarily affect the measuring sensor itself, and thus, the striven detection and exclusion for mitigation is not applicable in this case, alternatively the mitigation of the influence by special antenna setups is empirically analyzed. As a result, achievable accuracies in the sub-millimeter range can be demonstrated using exactly identical antenna setups. (2) By simulating generic obstruction scenarios, the influence on the positional accuracy of the deterioration of the satellite geometry, potentially caused by an elimination of satellite signals, can be identified as uncritical. Furthermore, a method for integrating measures for the quality of the satellite geometry in the waypoint planning of UAVs is developed, which enables the adaption and optimization of the flight route in the planning phase, as well as during the UAV flight. (3) Based on point clouds of terrestrial laser scanners, a method for the determination of elevation masks that are adaptive to the present antenna environment is developed, which enables an effective detection and exclusion of signals that are subject to NLOS reception or signal diffraction. This mitigation strategy can be applied to static and kinematic GNSS applications and by additionally integrating Fresnel zones, also the propagation characteristics of electromagnetic waves are considered. (4) As a preparatory step for the development of methods for detecting and excluding far-field multipath, the prerequisites for the occurrence of the effect are investigated. By comparison of simulated and observed SNR time series and by considering Fresnel zones, an overlap of 50% between Fresnel zone and reflecting surface can be identified as already being sufficient for potential far-field multipath influences. In the overall view, the findings and methods developed in this dissertation represent a relevant contribution to the superordinate goal of a holistic mitigation of site-dependent effects, and thus, enable a significant improvement of the positional accuracy under difficult GNSS conditions. In addition, this thesis adopts the currently forced trend from a pointwise to an area-based object acquisition by revealing and exploiting the potential of a detailed and efficient acquisition of the antenna environment by terrestrial laser scanners for mitigating and analyzing site-dependent effects in satellite based positioning applications