2012 UNH/NOAA Joint Hydrographic Center Performance and Progress Report

Report Period: 01/01/2012 – 12/31/201

Map-Based Localization for Unmanned Aerial Vehicle Navigation

Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%

Close-range photogrammetry and infrared imaging for non-invasive honeybee hive population assessment

16 p.Close-range photogrammetry and thermographic imaging techniques are used for the acquisition of all the data needed for the non-invasive assessment of a honeybee hive population. Temperature values complemented with precise 3D geometry generated using novel close-range photogrammetric and computer vision algorithms are used for the computation of the inner beehive temperature at each point of its surface. The methodology was validated through its application to three reference beehives with different population levels. The temperatures reached by the exterior surfaces of the hives showed a direct correlation with the population level. In addition, the knowledge of the 3D reality of the hives and the position of each temperature value allowed the positioning of the bee colonies without the need to open the hives. This way, the state of honeybee hives regarding the growth of population can be estimated without disturbing its natural development.S

Real-time geographical pose visualization system for the Augmented Reality Cloud

Denne oppgaven utforsker de underliggende teknologiene som muliggjør lokalisering og kartlegging i moderne Augmented Reality-systemer, såvel som eldre Augmented Reality-systemer. Spesielt er det satt fokus på Augmented Reality skyen og økosystemet rundt denne, en viktig del av fremtidig teknologisk infrastruktur som kommer til å muliggjøre persisterende, interoperatibel og fler-bruker Augmented Reality opplevelser. Videre, ved å bruke prinsipper fra Maskinsyn, GIS og Informasjonsteknologi, er et sanntids system utviklet for å visualisere mobile enheters geografiske posisjon og orientering på en virtuell globe i en WebGL kompatibel web-leser. Teknologier anvedt inkluderer OpenCV, WebSockets, Cesium.js og Node.js på tre forskjellige platformer; web, server og Android.This thesis explores the technologies that power the tracking and mapping under the hood of modern Augmented Reality systems, as well as previous iterations of Mobile Augmented Reality. Special attention is given to the so-called Augmented Reality Cloud and the eco-system around it, a crucial infrastructure piece for enabling persistent, interoperable and multi-user Augmented Reality experiences. Furthermore, by using principals from Computer Vision, GIS and Information Technology, a real-time system is developed that visualizes mobile device geopose on a virtual globe running in a WebGL enabled browser. Technologies applied include OpenCV, WebSockets, Cesium.js and Node.js on in three different environments; web, server and Android

Defining the Pose of any 3D Rigid Object and an Associated Distance

The pose of a rigid object is usually regarded as a rigid transformation, described by a translation and a rotation. However, equating the pose space with the space of rigid transformations is in general abusive, as it does not account for objects with proper symmetries -- which are common among man-made objects.In this article, we define pose as a distinguishable static state of an object, and equate a pose with a set of rigid transformations. Based solely on geometric considerations, we propose a frame-invariant metric on the space of possible poses, valid for any physical rigid object, and requiring no arbitrary tuning. This distance can be evaluated efficiently using a representation of poses within an Euclidean space of at most 12 dimensions depending on the object's symmetries. This makes it possible to efficiently perform neighborhood queries such as radius searches or k-nearest neighbor searches within a large set of poses using off-the-shelf methods. Pose averaging considering this metric can similarly be performed easily, using a projection function from the Euclidean space onto the pose space. The practical value of those theoretical developments is illustrated with an application of pose estimation of instances of a 3D rigid object given an input depth map, via a Mean Shift procedure

Augmented reality in city

V posledních letech zažila oblast rozšířené reality (AR) mimořádný růst, podpořený inovacemi v geoprostorových technologiích. Tato diplomová práce představuje aplikaci, která využívá Geospatial API - technologii uvedenou na trh v květnu 2022 - pro vizualizaci podzemních inženýrských sítí v ulicích města Prahy. Aplikace je založena na datech poskytnutých Institutem plánování a rozvoje Prahy. Úvodní výzkum se soustředí na hlubší pochopení těchto dat a na zkoumání AR technologií vhodných pro jejich efektivní vizualizaci. V následující fázi návrhu jsou podrobně popsány požadované funkce a vzhled aplikace. Tato fáze je následována detailním popisem procesu implementace. Závěrečná část této práce hodnotí přesnost aplikace, její omezení a uživatelsé testování. Tímto poskytuje zhodnocení tohoto API.In recent years, augmented reality (AR) has seen remarkable growth, bolstered by advances in geospatial technology. This thesis presents a novel application that leverages the Geospatial API, launched in May 2022, to visualize underground utility lines in the streets of Prague. The data, provided by the Prague Institute of Planning and Development, serve as the basis for the AR application. Initial research focuses on understanding the data set and exploring AR technologies for effective visualization. The subsequent design phase details the intended features and appearance of the application. This is followed by a discussion of the step-by-step implementation process. Finally, the thesis evaluates the application performance, limitations and user testing, providing insight into the capabilities of the API