Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

IM2ELEVATION: Building Height Estimation from Single-View Aerial Imagery

Estimation of the Digital Surface Model (DSM) and building heights from single-view aerial imagery is a challenging inherently ill-posed problem that we address in this paper by resorting to machine learning. We propose an end-to-end trainable convolutional-deconvolutional deep neural network architecture that enables learning mapping from a single aerial imagery to a DSM for analysis of urban scenes. We perform multisensor fusion of aerial optical and aerial light detection and ranging (Lidar) data to prepare the training data for our pipeline. The dataset quality is key to successful estimation performance. Typically, a substantial amount of misregistration artifacts are present due to georeferencing/projection errors, sensor calibration inaccuracies, and scene changes between acquisitions. To overcome these issues, we propose a registration procedure to improve Lidar and optical data alignment that relies on Mutual Information, followed by Hough transform-based validation step to adjust misregistered image patches. We validate our building height estimation model on a high-resolution dataset captured over central Dublin, Ireland: Lidar point cloud of 2015 and optical aerial images from 2017. These data allow us to validate the proposed registration procedure and perform 3D model reconstruction from single-view aerial imagery. We also report state-of-the-art performance of our proposed architecture on several popular DSM estimation datasets

Efficient Building Extraction for High Spatial Resolution Images Based on Dual Attention Network

Building extraction with high spatial resolution images becomes an important research in the field of computer vision for urban-related applications. Due to the rich detailed information and complex texture features presented in high spatial resolution images, the distribution of buildings is non-proportional and their difference of scales is obvious. General methods often provide confusion results with other ground objects. In this paper, a building extraction framework based on deep residual neural network with a self-attention mechanism is proposed. This mechanism contains two parts: one is the spatial attention module, which is used to aggregate and relate the local and global features at each position (short and long distance context information) of buildings; the other is channel attention module, in which the representation of comprehensive features (includes color, texture, geometric and high-level semantic feature) are improved. The combination of the dual attention modules makes buildings can be extracted from the complex backgrounds. The effectiveness of our method is validated by the experiments counted on a wide range high spatial resolution image, i.e., Jilin-1 Gaofen 02A imagery. Compared with some state-of-the-art segmentation methods, i.e., DeepLab-v3+, PSPNet, and PSANet algorithms, the proposed dual attention network-based method achieved high accuracy and intersection-over-union for extraction performance and show finest recognition integrity of buildings

Topological place recognition for life-long visual localization

Premio Extraordinario de Doctorado de la UAH en el año académico 2016-2017La navegación de vehículos inteligentes o robots móviles en períodos largos de tiempo ha experimentado un gran interés por parte de la comunidad investigadora en los últimos años. Los sistemas basados en cámaras se han extendido ampliamente en el pasado reciente gracias a las mejoras en sus características, precio y reducción de tamaño, añadidos a los progresos en técnicas de visión artificial. Por ello, la localización basada en visión es una aspecto clave para desarrollar una navegación autónoma robusta en situaciones a largo plazo. Teniendo en cuenta esto, la identificación de localizaciones por medio de técnicas de reconocimiento de lugar topológicas puede ser complementaria a otros enfoques como son las soluciones basadas en el Global Positioning System (GPS), o incluso suplementaria cuando la señal GPS no está disponible.El estado del arte en reconocimiento de lugar topológico ha mostrado un funcionamiento satisfactorio en el corto plazo. Sin embargo, la localización visual a largo plazo es problemática debido a los grandes cambios de apariencia que un lugar sufre como consecuencia de elementos dinámicos, la iluminación o la climatología, entre otros. El objetivo de esta tesis es enfrentarse a las dificultades de llevar a cabo una localización topológica eficiente y robusta a lo largo del tiempo. En consecuencia, se van a contribuir dos nuevos enfoques basados en reconocimiento visual de lugar para resolver los diferentes problemas asociados a una localización visual a largo plazo. Por un lado, un método de reconocimiento de lugar visual basado en descriptores binarios es propuesto. La innovación de este enfoque reside en la descripción global de secuencias de imágenes como códigos binarios, que son extraídos mediante un descriptor basado en la técnica denominada Local Difference Binary (LDB). Los descriptores son eficientemente asociados usando la distancia de Hamming y un método de búsqueda conocido como Approximate Nearest Neighbors (ANN). Además, una técnica de iluminación invariante es aplicada para mejorar el funcionamiento en condiciones luminosas cambiantes. El empleo de la descripción binaria previamente introducida proporciona una reducción de los costes computacionales y de memoria.Por otro lado, también se presenta un método de reconocimiento de lugar visual basado en deep learning, en el cual los descriptores aplicados son procesados por una Convolutional Neural Network (CNN). Este es un concepto recientemente popularizado en visión artificial que ha obtenido resultados impresionantes en problemas de clasificación de imagen. La novedad de nuestro enfoque reside en la fusión de la información de imagen de múltiples capas convolucionales a varios niveles y granularidades. Además, los datos redundantes de los descriptores basados en CNNs son comprimidos en un número reducido de bits para una localización más eficiente. El descriptor final es condensado aplicando técnicas de compresión y binarización para realizar una asociación usando de nuevo la distancia de Hamming. En términos generales, los métodos centrados en CNNs mejoran la precisión generando representaciones visuales de las localizaciones más detalladas, pero son más costosos en términos de computación.Ambos enfoques de reconocimiento de lugar visual son extensamente evaluados sobre varios datasets públicos. Estas pruebas arrojan una precisión satisfactoria en situaciones a largo plazo, como es corroborado por los resultados mostrados, que comparan nuestros métodos contra los principales algoritmos del estado del arte, mostrando mejores resultados para todos los casos.Además, también se ha analizado la aplicabilidad de nuestro reconocimiento de lugar topológico en diferentes problemas de localización. Estas aplicaciones incluyen la detección de cierres de lazo basada en los lugares reconocidos o la corrección de la deriva acumulada en odometría visual usando la información proporcionada por los cierres de lazo. Asimismo, también se consideran las aplicaciones de la detección de cambios geométricos a lo largo de las estaciones del año, que son esenciales para las actualizaciones de los mapas en sistemas de conducción autónomos centrados en una operación a largo plazo. Todas estas contribuciones son discutidas al final de la tesis, incluyendo varias conclusiones sobre el trabajo presentado y líneas de investigación futuras

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed

Label Efficient 3D Scene Understanding

3D scene understanding models are becoming increasingly integrated into modern society. With applications ranging from autonomous driving, Augmented Real- ity, Virtual Reality, robotics and mapping, the demand for well-behaved models is rapidly increasing. A key requirement for training modern 3D models is high- quality manually labelled training data. Collecting training data is often the time and monetary bottleneck, limiting the size of datasets. As modern data-driven neu- ral networks require very large datasets to achieve good generalisation, finding al- ternative strategies to manual labelling is sought after for many industries. In this thesis, we present a comprehensive study on achieving 3D scene under- standing with fewer labels. Specifically, we evaluate 4 approaches: existing data, synthetic data, weakly-supervised and self-supervised. Existing data looks at the potential of using readily available national mapping data as coarse labels for train- ing a building segmentation model. We further introduce an energy-based active contour snake algorithm to improve label quality by utilising co-registered LiDAR data. This is attractive as whilst the models may still require manual labels, these labels already exist. Synthetic data also exploits already existing data which was not originally designed for training neural networks. We demonstrate a pipeline for generating a synthetic Mobile Laser Scanner dataset. We experimentally evalu- ate if such a synthetic dataset can be used to pre-train smaller real-world datasets, increasing the generalisation with less data. A weakly-supervised approach is presented which allows for competitive per- formance on challenging real-world benchmark 3D scene understanding datasets with up to 95% less data. We propose a novel learning approach where the loss function is learnt. Our key insight is that the loss function is a local function and therefore can be trained with less data on a simpler task. Once trained our loss function can be used to train a 3D object detector using only unlabelled scenes. Our method is both flexible and very scalable, even performing well across datasets. Finally, we propose a method which only requires a single geometric represen- tation of each object class as supervision for 3D monocular object detection. We discuss why typical L2-like losses do not work for 3D object detection when us- ing differentiable renderer-based optimisation. We show that the undesirable local- minimas that the L2-like losses fall into can be avoided with the inclusion of a Generative Adversarial Network-like loss. We achieve state-of-the-art performance on the challenging 6DoF LineMOD dataset, without any scene level labels

Can linguistic features extracted from geo-referenced tweets help building function classification in remote sensing?

The fusion of two or more different data sources is a widely accepted technique in remote sensing while becoming increasingly important due to the availability of big Earth Observation satellite data. As a complementary source of geo-information to satellite data, massive text messages from social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Despite the uncontrolled quality: can linguistic features extracted from geo-referenced tweets support remote sensing tasks? This work presents a straightforward decision fusion framework for very high-resolution remote sensing images and Twitter text messages. We apply our proposed fusion framework to a land-use classification task - the building function classification task - in which we classify building functions like commercial or residential based on linguistic features derived from tweets and remote sensing images. Using building tags from OpenStreetMap (OSM), we labeled tweets and very high-resolution (VHR) images from Google Maps. We collected English tweets from San Francisco, New York City, Los Angeles, and Washington D.C. and trained a stacked bi-directional LSTM neural network with these tweets. For the aerial images, we predicted building functions with state-of-the-art Convolutional Neural Network (CNN) architectures fine-tuned from ImageNet on the given task. After predicting each modality separately, we combined the prediction probabilities of both models building-wise at a decision level. We show that the proposed fusion framework can improve the classification results of the building type classification task. To the best of our knowledge, we are the first to use semantic contents of Twitter messages and fusing them with remote sensing images to classify building functions at a single building level

Deep Neural Network Regression for Normalized Digital Surface Model Generation with Sentinel-2 Imagery

In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7%