Decentralized Riemannian Particle Filtering with Applications to Multi-Agent Localization

The primary focus of this research is to develop consistent nonlinear decentralized particle filtering approaches to the problem of multiple agent localization. A key aspect in our development is the use of Riemannian geometry to exploit the inherently non-Euclidean characteristics that are typical when considering multiple agent localization scenarios. A decentralized formulation is considered due to the practical advantages it provides over centralized fusion architectures. Inspiration is taken from the relatively new field of information geometry and the more established research field of computer vision. Differential geometric tools such as manifolds, geodesics, tangent spaces, exponential, and logarithmic mappings are used extensively to describe probabilistic quantities. Numerous probabilistic parameterizations were identified, settling on the efficient square-root probability density function parameterization. The square-root parameterization has the benefit of allowing filter calculations to be carried out on the well studied Riemannian unit hypersphere. A key advantage for selecting the unit hypersphere is that it permits closed-form calculations, a characteristic that is not shared by current solution approaches. Through the use of the Riemannian geometry of the unit hypersphere, we are able to demonstrate the ability to produce estimates that are not overly optimistic. Results are presented that clearly show the ability of the proposed approaches to outperform current state-of-the-art decentralized particle filtering methods. In particular, results are presented that emphasize the achievable improvement in estimation error, estimator consistency, and required computational burden

Visual SLAM from image sequences acquired by unmanned aerial vehicles

This thesis shows that Kalman filter based approaches are sufficient for the task of simultaneous localization and mapping from image sequences acquired by unmanned aerial vehicles. Using solely direction measurements to solve the problem of simultaneous localization and mapping (SLAM) is an important part of autonomous systems. Because the need for real-time capable systems, recursive estimation techniques, Kalman filter based approaches are the main focus of interest. Unfortunately, the non-linearity of the triangulation using the direction measurements cause decrease of accuracy and consistency of the results. The first contribution of this work is a general derivation of the recursive update of the Kalman filter. This derivation is based on implicit measurement equations, having the classical iterative non-linear as well as the non-iterative and linear Kalman filter as specializations of our general derivation. Second, a new formulation of linear-motion models for the single camera state model and the sliding window camera state model are given, that make it possible to compute the prediction in a fully linear manner. The third major contribution is a novel method for the initialization of new object points in the Kalman filter. Empirical studies using synthetic and real data of an image sequence of a photogrammetric strip are made, that demonstrate and compare the influences of the initialization methods of new object points in the Kalman filter. Forth, the accuracy potential of monoscopic image sequences from unmanned aerial vehicles for autonomous localization and mapping is theoretically analyzed, which can be used for planning purposes.Visuelle gleichzeitige Lokalisierung und Kartierung aus Bildfolgen von unbemannten Flugkörpern Diese Arbeit zeigt, dass die Kalmanfilter basierte Lösung der Triangulation zur Lokalisierung und Kartierung aus Bildfolgen von unbemannten Flugkörpern realisierbar ist. Aufgrund von Echtzeitanforderungen autonomer Systeme erreichen rekursive Schätz-verfahren, insbesondere Kalmanfilter basierte Ansätze, große Beliebheit. Bedauerlicherweise treten dabei durch die Nichtlinearität der Triangulation einige Effekte auf, welche die Konsistenz und Genauigkeit der Lösung hinsichtlich der geschätzten Parameter maßgeblich beeinflussen. Der erste Beitrag dieser Arbeit besteht in der Herleitung eines generellen Verfahrens zum rekursiven Verbessern im Kalmanfilter mit impliziten Beobachtungsgleichungen. Wir zeigen, dass die klassischen Verfahren im Kalmanfilter eine Spezialisierung unseres Ansatzes darstellen. Im zweiten Beitrag erweitern wir die klassische Modellierung für ein Einkameramodell zu einem Mehrkameramodell im Kalmanfilter. Diese Erweiterung erlaubt es uns, die Prädiktion für eine lineares Bewegungsmodell vollkommen linear zu berechnen. In einem dritten Hauptbeitrag stellen wir ein neues Verfahren zur Initialisierung von Neupunkten im Kalmanfilter vor. Anhand von empirischen Untersuchungen unter Verwendung simulierter und realer Daten einer Bildfolge eines photogrammetrischen Streifens zeigen und vergleichen wir, welchen Einfluß die Initialisierungsmethoden für Neupunkte im Kalmanfilter haben und welche Genauigkeiten für diese Szenarien erreichbar sind. Am Beispiel von Bildfolgen eines unbemannten Flugkörpern zeigen wir in dieser Arbeit als vierten Beitrag, welche Genauigkeit zur Lokalisierung und Kartierung durch Triangulation möglich ist. Diese theoretische Analyse kann wiederum zu Planungszwecken verwendet werden

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Computational time analysis in extended kalman filter based simultaneous localization and mapping

The simultaneous localization and mapping (SLAM) of a mobile robot is one of the applications that use estimation techniques. SLAM is a navigation technique that allows a mobile robot to navigate around autonomously while observing its surroundings in an unfamiliar environment. SLAM does not require a priori map, instead the mobile robot creates a map of the area incrementally with the help of sensors on board and uses this map to localize its location Due to its relatively easy algorithm and efficiency of estimation via the representation of the belief by a multivariate Gaussian distribution and a unimodal distribution, with a single mean annotated and corresponding covariance uncertainty, the extended Kalman filter (EKF) has become one of the most preferred estimators in mobile robot SLAM. However, due to the update process of the covariance matrix, EKF-based SLAM has high computational time. In SLAM, if more observation is being made by mobile robot, the state covariance size will be increasing. This eventually requires more memory and processing time due to excessive computation needs to be calculated over time. Therefore there is a need of enhancing the estimation performance by reducing the computational time in SLAM. Three phases involve in this research methodology which the first is theoretical formulation of the mobile robot model. This is followed by the environment and estimation method used to solve the SLAM of mobile robot. Simulation analysis was used to verify the findings. This research attempts to introduce a new approach to simplify the structure of the covariance matrix using the eigenvalues matrix diagonalization method. Through simulation result it is proved that time taken to complete the SLAM process using diagonalized covariance was reduced as compared to the normal covariance. However, there is one limitation encountered from this method in which the covariance values become too small, that indicates an optimistic estimation. For this reason, second objective is motivated to improve the optimistic problem. Addition of new element into the diagonal matrix, which is known as a pseudo element, is also investigated in this study. Via mathematical approach, these problems are discussed and explored from estimation-theoretic point of view. Through adding the pseudo noise element into diagonalized covariance, the optimistic condition of covariance matrix can be improved. This was shown through the increased size of covariance ellipses at the end of simulation process. Based on the findings it can be concluded that the addition of pseudo matrix in the updated state covariance can further improved the computational time for mobile robot estimation

Supporting Distributed Geo-Processing: A Framework for Managing Multi-Accuracy Spatial Data

Negli ultimi anni molti paesi hanno sviluppato un'infrastruttura tecnologica al fine di gestire i propri dati geografici (Spatial Data Infrastructure, SDI). Tali infrastrutture rechiedono nuove ed efficati metodologie per integrare continuamente dati che provengoono da sorgenti diverse e sono caratterizzati da diversi livelli di qualit\ue0. Questo bisogno \ue8 riconosciuto in letteratura ed \ue8 noto come problema di integrazione del dato (data integration) o fusione di informazioni (information fusion). Un aspetto peculiare dell'integrazione del dato geografico riguarda il matching e l'allineamento degli oggetti geometrici. I metodi esistenti solitamente eseguono l'integrazione semplicemente allineando il database meno accurato con quello pi\uf9 accurato, assumendo che il secondo contenga sempre una rappresentazione migliore delle geometrie rilevate. Seguendo questo approccio, gli oggetti geografici sono combinati assieme in una maniera non ottimale, causando distorsioni che potenzialmente riducono la qualit\ue0 complessiva del database finale. Questa tesi si occupa del problema dell'integrazione del dato spaziale all'interno di una SDI fortemente strutturata, in cui i membri hanno preventivamente aderito ad uno schema globale comune, pertanto si focalizza sul problema dell'integrazione geometrica, assumendo che precedenti operazioni di integrazione sullo schema siano gi\ue0 state eseguire. In particulare, la tesi inizia proponendo un modello per la rappresentazione dell'informazione spaziale caratterizzata da differenti livelli di qualit\ue0, quindi definisce un processo di integrazione che tiene conto dell'accuratezza delle posizioni contenute in entrambi i database coinvoilti. La tecnica di integrazione proposta rappresenta la base per un framework capace di supportare il processamento distributo di dati geografici (geo-processing) nel contesto di una SDI. Il problema di implementare tale computazione distribuita e di lunga durata \ue8 trattato anche da un punto di vista pratico attraverso la valutazione dell'applicabilit\ue0 delle tecnologie di workflow esistenti. Tale valutazione ha portato alla definizione di una soluzione software ideale, le cui caratteristiche sono discusse negli ultimi capitoli, considerando come caso di studio il design del processo di integrazione proposto.In the last years many countries have developed a Spatial Data Infrastructure (SDI) to manage their geographical information. Large SDIs require new effective techniques to continuously integrate spatial data coming from different sources and characterized by different quality levels. This need is recognized in the scientific literature and is known as data integration or information fusion problem. A specific aspect of spatial data integration concerns the matching and alignment of object geometries. Existing methods mainly perform the integration by simply aligning the less accurate database with the more accurate one, assuming that the latter always contains a better representation of the relevant geometries. Following this approach, spatial entities are merged together in a sub-optimal manner, causing distortions that potentially reduce the overall database quality. This thesis deals with the problem of spatial data integration in a highly-coupled SDI where members have already adhered to a common global schema, hence it focuses on the geometric integration problem assuming that some schema matching operations have already been performed. In particular, the thesis initially proposes a model for representing spatial data together with their quality characteristics, producing a multi-accuracy spatial database, then it defines a novel integration process that takes care of the different positional accuracies of the involved source databases. The main goal of such process is to preserve coherence and consistency of the integrated data and when possible enhancing its accuracy. The proposed multi-accuracy spatial data model and the related integration technique represent the basis for a framework able to support distributed geo-processing in a SDI context. The problem of implementing such long-running distributed computations is also treated from a practical perspective by evaluating the applicability of existing workflow technologies. This evaluation leads to the definition of an ideal software solution, whose characteristics are discussed in the last chapters by considering the design of the proposed integration process as a motivating example

A hybrid approach to simultaneous localization and mapping in indoors environment

This thesis will present SLAM in the current literature to benefit from then it will present the investigation results for a hybrid approach used where different algorithms using laser, sonar, and camera sensors were tested and compared. The contribution of this thesis is the development of a hybrid approach for SLAM that uses different sensors and where different factors are taken into consideration such as dynamic objects, and the development of a scalable grid map model with new sensors models for real time update of the map.The thesis will show the success found, difficulties faced and limitations of the algorithms developed which were simulated and experimentally tested in an indoors environment

Robot Localization with Weak Maps

In this work, we present an approach for indoor localization for a mobile robot based on a weakly-defined prior map. The aim is to estimate the robot's pose where even an incomplete knowledge of the environment is available, and furthermore, improving the information in the prior map according to measurements. We discuss two different approaches to describe the prior map. In the first approach, a complete map of the environment is given to the robot, but the scale of the map is unknown. The map is represented by occupancy grid mapping. We present a method based on Monte Carlo localization that successfully estimates the robot's pose and the scale of the map. In the second approach, the prior map is a 2D sketched map provided by a user, and it does not hold exact metric information of the building. Moreover, some obstacles and features are not fully presented. The aim is to estimate the scale of the map and to modify and correct the prior map knowing the robot's exact pose. The map is represented in the polygonal format in the homogeneous coordinates, and is capable of analyzing the uncertainty of features. We propose two methods to update prior information in the map. One uses a Kalman filter, and the other is based on Geometrical Constraints. Both methods can partially improve the estimate of the dimensions of rooms and locations and the orientation of walls, but they slightly suffer from data association