Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations

Size, weight, and power constrained platforms impose constraints on computational resources that introduce unique challenges in implementing localization algorithms. We present a framework to perform fast localization on such platforms enabled by the compressive capabilities of Gaussian Mixture Model representations of point cloud data. Given raw structural data from a depth sensor and pitch and roll estimates from an on-board attitude reference system, a multi-hypothesis particle filter localizes the vehicle by exploiting the likelihood of the data originating from the mixture model. We demonstrate analysis of this likelihood in the vicinity of the ground truth pose and detail its utilization in a particle filter-based vehicle localization strategy, and later present results of real-time implementations on a desktop system and an off-the-shelf embedded platform that outperform localization results from running a state-of-the-art algorithm on the same environment

Inertial navigation aided by simultaneous loacalization and mapping

Unmanned aerial vehicles technologies are getting smaller and cheaper to use and the challenges of payload limitation in unmanned aerial vehicles are being overcome. Integrated navigation system design requires selection of set of sensors and computation power that provides reliable and accurate navigation parameters (position, velocity and attitude) with high update rates and bandwidth in small and cost effective manner. Many of today’s operational unmanned aerial vehicles navigation systems rely on inertial sensors as a primary measurement source. Inertial Navigation alone however suffers from slow divergence with time. This divergence is often compensated for by employing some additional source of navigation information external to Inertial Navigation. From the 1990’s to the present day Global Positioning System has been the dominant navigation aid for Inertial Navigation. In a number of scenarios, Global Positioning System measurements may be completely unavailable or they simply may not be precise (or reliable) enough to be used to adequately update the Inertial Navigation hence alternative methods have seen great attention. Aiding Inertial Navigation with vision sensors has been the favoured solution over the past several years. Inertial and vision sensors with their complementary characteristics have the potential to answer the requirements for reliable and accurate navigation parameters. In this thesis we address Inertial Navigation position divergence. The information for updating the position comes from combination of vision and motion. When using such a combination many of the difficulties of the vision sensors (relative depth, geometry and size of objects, image blur and etc.) can be circumvented. Motion grants the vision sensors with many cues that can help better to acquire information about the environment, for instance creating a precise map of the environment and localize within the environment. We propose changes to the Simultaneous Localization and Mapping augmented state vector in order to take repeated measurements of the map point. We show that these repeated measurements with certain manoeuvres (motion) around or by the map point are crucial for constraining the Inertial Navigation position divergence (bounded estimation error) while manoeuvring in vicinity of the map point. This eliminates some of the uncertainty of the map point estimates i.e. it reduces the covariance of the map points estimates. This concept brings different parameterization (feature initialisation) of the map points in Simultaneous Localization and Mapping and we refer to it as concept of aiding Inertial Navigation by Simultaneous Localization and Mapping. We show that making such an integrated navigation system requires coordination with the guidance and control measurements and the vehicle task itself for performing the required vehicle manoeuvres (motion) and achieving better navigation accuracy. This fact brings new challenges to the practical design of these modern jam proof Global Positioning System free autonomous navigation systems. Further to the concept of aiding Inertial Navigation by Simultaneous Localization and Mapping we have investigated how a bearing only sensor such as single camera can be used for aiding Inertial Navigation. The results of the concept of Inertial Navigation aided by Simultaneous Localization and Mapping were used. New parameterization of the map point in Bearing Only Simultaneous Localization and Mapping is proposed. Because of the number of significant problems that appear when implementing the Extended Kalman Filter in Inertial Navigation aided by Bearing Only Simultaneous Localization and Mapping other algorithms such as Iterated Extended Kalman Filter, Unscented Kalman Filter and Particle Filters were implemented. From the results obtained, the conclusion can be drawn that the nonlinear filters should be the choice of estimators for this application

High Speed Event Camera TRacking

Event cameras are bioinspired sensors with reaction times in the order of microseconds. This property makes them appealing for use in highly-dynamic computer vision applications. In this work,we explore the limits of this sensing technology and present an ultra-fast tracking algorithm able to estimate six-degree-of-freedom motion with dynamics over 25.8 g, at a throughput of 10 kHz,processing over a million events per second. Our method is capable of tracking either camera motion or the motion of an object in front of it, using an error-state Kalman filter formulated in a Lie-theoretic sense. The method includes a robust mechanism for the matching of events with projected line segments with very fast outlier rejection. Meticulous treatment of sparse matrices is applied to achieve real-time performance. Different motion models of varying complexity are considered for the sake of comparison and performance analysi

Vision and SLAM on a highly dynamic mobile two-wheeled robot

This thesis examines a sparse feature based visual monocular simultaneous localization and mapping (SLAM) approach with the intension of stabilizing a two-wheeled balancing robot. The first part introduces the basics like camera geometry, image processing and filtering. Further on, the thesis treats the details of a monocular SLAM system and shows some specialties to keep the computational effort low. The last part deals with Andrew Davison's "SceneLib" library and how it can be used to obtain the camera state vector.Die vorliegende Arbeit gibt einen Einblick in das Thema der auf wenigen Bildfeatures basierenden simultanen Lokalisierung und Karten Erstellung (SLAM) mittels monokularer Kamera zum Zwecke der Regelung eines zweirädrigen balancierenden Roboters. Im ersten Teil werden grundlegende Themen wie die Kamerageometrie, Bildverarbeitung und Filtertechniken besprochen. Darauf aufbauend werden im zweiten Abschnitt Details und effizienzsteigernde Maßnahmen erläutert, die ein monokulares Echtzeit-Kamera-SLAM System möglich machen. Im letzten Teil der Arbeit wird beschrieben wie mittels Andrew Davisons "SceneLib" Bibliothek die aktuelle Kamera Pose bestimmt werden kann

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

Advances in Simultaneous Localization and Mapping in Confined Underwater Environments Using Sonar and Optical Imaging.

This thesis reports on the incorporation of surface information into a probabilistic simultaneous localization and mapping (SLAM) framework used on an autonomous underwater vehicle (AUV) designed for underwater inspection. AUVs operating in cluttered underwater environments, such as ship hulls or dams, are commonly equipped with Doppler-based sensors, which---in addition to navigation---provide a sparse representation of the environment in the form of a three-dimensional (3D) point cloud. The goal of this thesis is to develop perceptual algorithms that take full advantage of these sparse observations for correcting navigational drift and building a model of the environment. In particular, we focus on three objectives. First, we introduce a novel representation of this 3D point cloud as collections of planar features arranged in a factor graph. This factor graph representation probabalistically infers the spatial arrangement of each planar segment and can effectively model smooth surfaces (such as a ship hull). Second, we show how this technique can produce 3D models that serve as input to our pipeline that produces the first-ever 3D photomosaics using a two-dimensional (2D) imaging sonar. Finally, we propose a model-assisted bundle adjustment (BA) framework that allows for robust registration between surfaces observed from a Doppler sensor and visual features detected from optical images. Throughout this thesis, we show methods that produce 3D photomosaics using a combination of triangular meshes (derived from our SLAM framework or given a-priori), optical images, and sonar images. Overall, the contributions of this thesis greatly increase the accuracy, reliability, and utility of in-water ship hull inspection with AUVs despite the challenges they face in underwater environments. We provide results using the Hovering Autonomous Underwater Vehicle (HAUV) for autonomous ship hull inspection, which serves as the primary testbed for the algorithms presented in this thesis. The sensor payload of the HAUV consists primarily of: a Doppler velocity log (DVL) for underwater navigation and ranging, monocular and stereo cameras, and---for some applications---an imaging sonar.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120750/1/paulozog_1.pd

Keyframe-based monocular SLAM: design, survey, and future directions

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery

A hybrid visual-based SLAM architecture: local filter-based SLAM with keyframe-based global mapping

This work presents a hybrid visual-based SLAM architecture that aims to take advantage of the strengths of each of the two main methodologies currently available for implementing visual-based SLAM systems, while at the same time minimizing some of their drawbacks. The main idea is to implement a local SLAM process using a filter-based technique, and enable the tasks of building and maintaining a consistent global map of the environment, including the loop closure problem, to use the processes implemented using optimization-based techniques. Different variants of visual-based SLAM systems can be implemented using the proposed architecture. This work also presents the implementation case of a full monocular-based SLAM system for unmanned aerial vehicles that integrates additional sensory inputs. Experiments using real data obtained from the sensors of a quadrotor are presented to validate the feasibility of the proposed approachPostprint (published version