976 research outputs found

    Spatio-Temporal Calibration for Omni-Directional Vehicle-Mounted

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    We present a solution to the problem of spatio-temporal calibration for event cameras mounted on an onmi-directional vehicle. Different from traditional methods that typically determine the camera's pose with respect to the vehicle's body frame using alignment of trajectories, our approach leverages the kinematic correlation of two sets of linear velocity estimates from event data and wheel odometers, respectively. The overall calibration task consists of estimating the underlying temporal offset between the two heterogeneous sensors, and furthermore, recovering the extrinsic rotation that defines the linear relationship between the two sets of velocity estimates. The first sub-problem is formulated as an optimization one, which looks for the optimal temporal offset that maximizes a correlation measurement invariant to arbitrary linear transformation. Once the temporal offset is compensated, the extrinsic rotation can be worked out with an iterative closed-form solver that incrementally registers associated linear velocity estimates. The proposed algorithm is proved effective on both synthetic data and real data, outperforming traditional methods based on alignment of trajectories

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells

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    Learning-based multi-view stereo (MVS) methods deal with predicting accurate depth maps to achieve an accurate and complete 3D representation. Despite the excellent performance, existing methods ignore the fact that a suitable depth geometry is also critical in MVS. In this paper, we demonstrate that different depth geometries have significant performance gaps, even using the same depth prediction error. Therefore, we introduce an ideal depth geometry composed of Saddle-Shaped Cells, whose predicted depth map oscillates upward and downward around the ground-truth surface, rather than maintaining a continuous and smooth depth plane. To achieve it, we develop a coarse-to-fine framework called Dual-MVSNet (DMVSNet), which can produce an oscillating depth plane. Technically, we predict two depth values for each pixel (Dual-Depth), and propose a novel loss function and a checkerboard-shaped selecting strategy to constrain the predicted depth geometry. Compared to existing methods,DMVSNet achieves a high rank on the DTU benchmark and obtains the top performance on challenging scenes of Tanks and Temples, demonstrating its strong performance and generalization ability. Our method also points to a new research direction for considering depth geometry in MVS.Comment: Accepted by ICCV 202

    Spectral Graphormer: Spectral Graph-based Transformer for Egocentric Two-Hand Reconstruction using Multi-View Color Images

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    We propose a novel transformer-based framework that reconstructs two high fidelity hands from multi-view RGB images. Unlike existing hand pose estimation methods, where one typically trains a deep network to regress hand model parameters from single RGB image, we consider a more challenging problem setting where we directly regress the absolute root poses of two-hands with extended forearm at high resolution from egocentric view. As existing datasets are either infeasible for egocentric viewpoints or lack background variations, we create a large-scale synthetic dataset with diverse scenarios and collect a real dataset from multi-calibrated camera setup to verify our proposed multi-view image feature fusion strategy. To make the reconstruction physically plausible, we propose two strategies: (i) a coarse-to-fine spectral graph convolution decoder to smoothen the meshes during upsampling and (ii) an optimisation-based refinement stage at inference to prevent self-penetrations. Through extensive quantitative and qualitative evaluations, we show that our framework is able to produce realistic two-hand reconstructions and demonstrate the generalisation of synthetic-trained models to real data, as well as real-time AR/VR applications.Comment: Accepted to ICCV 202

    Contributions to improve the technologies supporting unmanned aircraft operations

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    Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

    Towards Object-Centric Scene Understanding

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    Visual perception for autonomous agents continues to attract community attention due to the disruptive technologies and the wide applicability of such solutions. Autonomous Driving (AD), a major application in this domain, promises to revolutionize our approach to mobility while bringing critical advantages in limiting accident fatalities. Fueled by recent advances in Deep Learning (DL), more computer vision tasks are being addressed using a learning paradigm. Deep Neural Networks (DNNs) succeeded consistently in pushing performances to unprecedented levels and demonstrating the ability of such approaches to generalize to an increasing number of difficult problems, such as 3D vision tasks. In this thesis, we address two main challenges arising from the current approaches. Namely, the computational complexity of multi-task pipelines, and the increasing need for manual annotations. On the one hand, AD systems need to perceive the surrounding environment on different levels of detail and, subsequently, take timely actions. This multitasking further limits the time available for each perception task. On the other hand, the need for universal generalization of such systems to massively diverse situations requires the use of large-scale datasets covering long-tailed cases. Such requirement renders the use of traditional supervised approaches, despite the data readily available in the AD domain, unsustainable in terms of annotation costs, especially for 3D tasks. Driven by the AD environment nature and the complexity dominated (unlike indoor scenes) by the presence of other scene elements (mainly cars and pedestrians) we focus on the above-mentioned challenges in object-centric tasks. We, then, situate our contributions appropriately in fast-paced literature, while supporting our claims with extensive experimental analysis leveraging up-to-date state-of-the-art results and community-adopted benchmarks

    2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds

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    The commonly adopted detect-then-match approach to registration finds difficulties in the cross-modality cases due to the incompatible keypoint detection and inconsistent feature description. We propose, 2D3D-MATR, a detection-free method for accurate and robust registration between images and point clouds. Our method adopts a coarse-to-fine pipeline where it first computes coarse correspondences between downsampled patches of the input image and the point cloud and then extends them to form dense correspondences between pixels and points within the patch region. The coarse-level patch matching is based on transformer which jointly learns global contextual constraints with self-attention and cross-modality correlations with cross-attention. To resolve the scale ambiguity in patch matching, we construct a multi-scale pyramid for each image patch and learn to find for each point patch the best matching image patch at a proper resolution level. Extensive experiments on two public benchmarks demonstrate that 2D3D-MATR outperforms the previous state-of-the-art P2-Net by around 2020 percentage points on inlier ratio and over 1010 points on registration recall. Our code and models are available at https://github.com/minhaolee/2D3DMATR.Comment: Accepted by ICCV 202

    Visual place recognition for improved open and uncertain navigation

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    Visual place recognition localises a query place image by comparing it against a reference database of known place images, a fundamental element of robotic navigation. Recent work focuses on using deep learning to learn image descriptors for this task that are invariant to appearance changes from dynamic lighting, weather and seasonal conditions. However, these descriptors: require greater computational resources than are available on robotic hardware, have few SLAM frameworks designed to utilise them, return a relative comparison between image descriptors which is difficult to interpret, cannot be used for appearance invariance in other navigation tasks such as scene classification and are unable to identify query images from an open environment that have no true match in the reference database. This thesis addresses these challenges with three contributions. The first is a lightweight visual place recognition descriptor combined with a probabilistic filter to address a subset of the visual SLAM problem in real-time. The second contribution combines visual place recognition and scene classification for appearance invariant scene classification, which is extended to recognise unknown scene classes when navigating an open environment. The final contribution uses comparisons between query and reference image descriptors to classify whether they result in a true, or false positive localisation and whether a true match for the query image exists in the reference database.Edinburgh Centre for Robotics and Engineering and Physical Sciences Research Council (EPSRC) fundin

    Shaped-based IMU/Camera Tightly Coupled Object-level SLAM using Rao-Blackwellized Particle Filtering

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    Simultaneous Localization and Mapping (SLAM) is a decades-old problem. The classical solution to this problem utilizes entities such as feature points that cannot facilitate the interactions between a robot and its environment (e.g., grabbing objects). Recent advances in deep learning have paved the way to accurately detect objects in the image under various illumination conditions and occlusions. This led to the emergence of object-level solutions to the SLAM problem. Current object-level methods depend on an initial solution using classical approaches and assume that errors are Gaussian. This research develops a standalone solution to object-level SLAM that integrates the data from a monocular camera and an IMU (available in low-end devices) using Rao Blackwellized Particle Filter (RBPF). RBPF does not assume Gaussian distribution for the error; thus, it can handle a variety of scenarios (such as when a symmetrical object with pose ambiguities is encountered). The developed method utilizes shape instead of texture; therefore, texture-less objects can be incorporated into the solution. In the particle weighing process, a new method is developed that utilizes the Intersection over the Union (IoU) area of the observed and projected boundaries of the object that does not require point-to-point correspondence. Thus, it is not prone to false data correspondences. Landmark initialization is another important challenge for object-level SLAM. In the state-of-the-art delayed initialization, the trajectory estimation only relies on the motion model provided by IMU mechanization (during the initialization), leading to large errors. In this thesis, two novel undelayed initializations are developed. One relies only on a monocular camera and IMU, and the other utilizes an ultrasonic rangefinder as well. The developed object-level SLAM is tested using wheeled robots and handheld devices, and an error (in the position) of 4.1 to 13.1 cm (0.005 to 0.028 of the total path length) has been obtained through extensive experiments using only a single object. These experiments are conducted in different indoor environments under different conditions (e.g. illumination). Further, it is shown that undelayed initialization using an ultrasonic sensor can reduce the algorithm's runtime by half

    Scene representation and matching for visual localization in hybrid camera scenarios

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    Scene representation and matching are crucial steps in a variety of tasks ranging from 3D reconstruction to virtual/augmented/mixed reality applications, to robotics, and others. While approaches exist that tackle these tasks, they mostly overlook the issue of efficiency in the scene representation, which is fundamental in resource-constrained systems and for increasing computing speed. Also, they normally assume the use of projective cameras, while performance on systems based on other camera geometries remains suboptimal. This dissertation contributes with a new efficient scene representation method that dramatically reduces the number of 3D points. The approach sets up an optimization problem for the automated selection of the most relevant points to retain. This leads to a constrained quadratic program, which is solved optimally with a newly introduced variant of the sequential minimal optimization method. In addition, a new initialization approach is introduced for the fast convergence of the method. Extensive experimentation on public benchmark datasets demonstrates that the approach produces a compressed scene representation quickly while delivering accurate pose estimates. The dissertation also contributes with new methods for scene matching that go beyond the use of projective cameras. Alternative camera geometries, like fisheye cameras, produce images with very high distortion, making current image feature point detectors and descriptors less efficient, since designed for projective cameras. New methods based on deep learning are introduced to address this problem, where feature detectors and descriptors can overcome distortion effects and more effectively perform feature matching between pairs of fisheye images, and also between hybrid pairs of fisheye and perspective images. Due to the limited availability of fisheye-perspective image datasets, three datasets were collected for training and testing the methods. The results demonstrate an increase of the detection and matching rates which outperform the current state-of-the-art methods
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