MOMA: Visual Mobile Marker Odometry

In this paper, we present a cooperative odometry scheme based on the detection of mobile markers in line with the idea of cooperative positioning for multiple robots [1]. To this end, we introduce a simple optimization scheme that realizes visual mobile marker odometry via accurate fixed marker-based camera positioning and analyse the characteristics of errors inherent to the method compared to classical fixed marker-based navigation and visual odometry. In addition, we provide a specific UAV-UGV configuration that allows for continuous movements of the UAV without doing stops and a minimal caterpillar-like configuration that works with one UGV alone. Finally, we present a real-world implementation and evaluation for the proposed UAV-UGV configuration

Visual 3-D SLAM from UAVs

The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments. Every issue of the whole process is discussed in order to obtain more accurate localization and mapping from UAVs flights. Firstly, the issues related to the visual features of objects in the scene, their distance to the UAV, and the related image acquisition system and their calibration are evaluated for improving the whole process. Other important, considered issues are related to the image processing techniques, such as interest point detection, the matching procedure and the scaling factor. The whole system has been tested using the COLIBRI mini UAV in partially structured environments. The results that have been obtained for localization, tested against the GPS information of the flights, show that Visual SLAM delivers reliable localization and mapping that makes it suitable for some outdoors applications when flying UAVs

Vision-based localization methods under GPS-denied conditions

This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline map registration and lane vision detection schemes to achieve Absolute Visual Localization. This paper compares the performance and applications of mainstream methods for visual localization and provides suggestions for future studies.Comment: 32 pages, 15 figure

Event-Based Visual-Inertial Odometry on a Fixed-Wing Unmanned Aerial Vehicle

Event-based cameras are a new type of visual sensor that operate under a unique paradigm. These cameras provide asynchronous data on the log-level changes in light intensity for individual pixels, independent of other pixels\u27 measurements. Through the hardware-level approach to change detection, these cameras can achieve microsecond fidelity, millisecond latency, ultra-wide dynamic range, and all with very low power requirements. The advantages provided by event-based cameras make them excellent candidates for visual odometry (VO) for unmanned aerial vehicle (UAV) navigation. This document presents the research and implementation of an event-based visual inertial odometry (EVIO) pipeline, which estimates a vehicle\u27s 6-degrees-of-freedom (DOF) motion and pose utilizing an affixed event-based camera with an integrated Micro-Electro-Mechanical Systems (MEMS) inertial measurement unit (IMU). The front-end of the EVIO pipeline uses the current motion estimate of the pipeline to generate motion-compensated frames from the asynchronous event camera data. These frames are fed the back-end of the pipeline, which uses a Multi-State Constrained Kalman Filter (MSCKF) [1] implemented with Scorpion, a Bayesian state estimation framework developed by the Autonomy and Navigation Technology (ANT) Center at Air Force Institute of Technology (AFIT) [2]. This EVIO pipeline was tested on selections from the benchmark Event Camera Dataset [3]; and on a dataset collected, as part of this research, during the ANT Center\u27s first flight test with an event-based camera

Survey of computer vision algorithms and applications for unmanned aerial vehicles

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R)

Visual Odometry and Sparse Scene Reconstruction for UAVs with a Multi-Fisheye Camera System

Autonomously operating UAVs demand a fast localization for navigation, to actively explore unknown areas and to create maps. For pose estimation, many UAV systems make use of a combination of GPS receivers and inertial sensor units (IMU). However, GPS signal coverage may go down occasionally, especially in the close vicinity of objects, and precise IMUs are too heavy to be carried by lightweight UAVs. This and the high cost of high quality IMU motivate the use of inexpensive vision based sensors for localization using visual odometry or visual SLAM (simultaneous localization and mapping) techniques. The first contribution of this thesis is a more general approach to bundle adjustment with an extended version of the projective coplanarity equation which enables us to make use of omnidirectional multi-camera systems which may consist of fisheye cameras that can capture a large field of view with one shot. We use ray directions as observations instead of image points which is why our approach does not rely on a specific projection model assuming a central projection. In addition, our approach allows the integration and estimation of points at infinity, which classical bundle adjustments are not capable of. We show that the integration of far or infinitely far points stabilizes the estimation of the rotation angles of the camera poses. In its second contribution, we employ this approach to bundle adjustment in a highly integrated system for incremental pose estimation and mapping on light-weight UAVs. Based on the image sequences of a multi-camera system our system makes use of tracked feature points to incrementally build a sparse map and incrementally refines this map using the iSAM2 algorithm. Our system is able to optionally integrate GPS information on the level of carrier phase observations even in underconstrained situations, e.g. if only two satellites are visible, for georeferenced pose estimation. This way, we are able to use all available information in underconstrained GPS situations to keep the mapped 3D model accurate and georeferenced. In its third contribution, we present an approach for re-using existing methods for dense stereo matching with fisheye cameras, which has the advantage that highly optimized existing methods can be applied as a black-box without modifications even with cameras that have field of view of more than 180 deg. We provide a detailed accuracy analysis of the obtained dense stereo results. The accuracy analysis shows the growing uncertainty of observed image points of fisheye cameras due to increasing blur towards the image border. Core of the contribution is a rigorous variance component estimation which allows to estimate the variance of the observed disparities at an image point as a function of the distance of that point to the principal point. We show that this improved stochastic model provides a more realistic prediction of the uncertainty of the triangulated 3D points.Autonom operierende UAVs benötigen eine schnelle Lokalisierung zur Navigation, zur Exploration unbekannter Umgebungen und zur Kartierung. Zur Posenbestimmung verwenden viele UAV-Systeme eine Kombination aus GPS-Empfängern und Inertial-Messeinheiten (IMU). Die Verfügbarkeit von GPS-Signalen ist jedoch nicht überall gewährleistet, insbesondere in der Nähe abschattender Objekte, und präzise IMUs sind für leichtgewichtige UAVs zu schwer. Auch die hohen Kosten qualitativ hochwertiger IMUs motivieren den Einsatz von kostengünstigen bildgebenden Sensoren zur Lokalisierung mittels visueller Odometrie oder SLAM-Techniken zur simultanen Lokalisierung und Kartierung. Im ersten wissenschaftlichen Beitrag dieser Arbeit entwickeln wir einen allgemeineren Ansatz für die Bündelausgleichung mit einem erweiterten Modell für die projektive Kollinearitätsgleichung, sodass auch omnidirektionale Multikamerasysteme verwendet werden können, welche beispielsweise bestehend aus Fisheyekameras mit einer Aufnahme einen großen Sichtbereich abdecken. Durch die Integration von Strahlrichtungen als Beobachtungen ist unser Ansatz nicht von einem kameraspezifischen Abbildungsmodell abhängig solange dieses der Zentralprojektion folgt. Zudem erlaubt unser Ansatz die Integration und Schätzung von unendlich fernen Punkten, was bei klassischen Bündelausgleichungen nicht möglich ist. Wir zeigen, dass durch die Integration weit entfernter und unendlich ferner Punkte die Schätzung der Rotationswinkel der Kameraposen stabilisiert werden kann. Im zweiten Beitrag verwenden wir diesen entwickelten Ansatz zur Bündelausgleichung für ein System zur inkrementellen Posenschätzung und dünnbesetzten Kartierung auf einem leichtgewichtigen UAV. Basierend auf den Bildsequenzen eines Mulitkamerasystems baut unser System mittels verfolgter markanter Bildpunkte inkrementell eine dünnbesetzte Karte auf und verfeinert diese inkrementell mittels des iSAM2-Algorithmus. Unser System ist in der Lage optional auch GPS Informationen auf dem Level von GPS-Trägerphasen zu integrieren, wodurch sogar in unterbestimmten Situation - beispielsweise bei nur zwei verfügbaren Satelliten - diese Informationen zur georeferenzierten Posenschätzung verwendet werden können. Im dritten Beitrag stellen wir einen Ansatz zur Verwendung existierender Methoden für dichtes Stereomatching mit Fisheyekameras vor, sodass hoch optimierte existierende Methoden als Black Box ohne Modifzierungen sogar mit Kameras mit einem Gesichtsfeld von mehr als 180 Grad verwendet werden können. Wir stellen eine detaillierte Genauigkeitsanalyse basierend auf dem Ergebnis des dichten Stereomatchings dar. Die Genauigkeitsanalyse zeigt, wie stark die Genauigkeit beobachteter Bildpunkte bei Fisheyekameras zum Bildrand aufgrund von zunehmender Unschärfe abnimmt. Das Kernstück dieses Beitrags ist eine Varianzkomponentenschätzung, welche die Schätzung der Varianz der beobachteten Disparitäten an einem Bildpunkt als Funktion von der Distanz dieses Punktes zum Hauptpunkt des Bildes ermöglicht. Wir zeigen, dass dieses verbesserte stochastische Modell eine realistischere Prädiktion der Genauigkeiten der 3D Punkte ermöglicht

Visual navigation and path tracking using street geometry information for image alignment and servoing

Single camera-based navigation systems need information from other sensors or from the work environment to produce reliable and accurate position measurements. Providing such trustable, accurate, and available information in the environment is very important. The work highlights that the availability of well-described streets in urban environments can be exploited by drones for navigation and path tracking purposes, thus benefitting from such structures is not limited to only automated driving cars. While the drone position is continuously computed using visual odometry, scene matching is used to correct the position drift depending on some landmarks. The drone path is defined by several waypoints, and landmarks centralized by those waypoints are carefully chosen in the street intersections. The known streets’ geometry and dimensions are used to estimate the image scale and orientation which are necessary for images alignment, to compensate for the visual odometry drift, and to pass closer to the landmark center by the visual servoing process. Probabilistic Hough transform is used to detect and extract the street borders. The system is realized in a simulation environment consisting of the Robot Operating System ROS, 3D dynamic simulator Gazebo, and IRIS drone model. The results prove the suggested system efficiency with a 1.4 m position RMS error

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots