Network Uncertainty Informed Semantic Feature Selection for Visual SLAM

In order to facilitate long-term localization using a visual simultaneous localization and mapping (SLAM) algorithm, careful feature selection can help ensure that reference points persist over long durations and the runtime and storage complexity of the algorithm remain consistent. We present SIVO (Semantically Informed Visual Odometry and Mapping), a novel information-theoretic feature selection method for visual SLAM which incorporates semantic segmentation and neural network uncertainty into the feature selection pipeline. Our algorithm selects points which provide the highest reduction in Shannon entropy between the entropy of the current state and the joint entropy of the state, given the addition of the new feature with the classification entropy of the feature from a Bayesian neural network. Each selected feature significantly reduces the uncertainty of the vehicle state and has been detected to be a static object (building, traffic sign, etc.) repeatedly with a high confidence. This selection strategy generates a sparse map which can facilitate long-term localization. The KITTI odometry dataset is used to evaluate our method, and we also compare our results against ORB_SLAM2. Overall, SIVO performs comparably to the baseline method while reducing the map size by almost 70%.Comment: Published in: 2019 16th Conference on Computer and Robot Vision (CRV

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Jointly Optimizing Placement and Inference for Beacon-based Localization

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and Systems (IROS

Stochastic Signal Processing and Power Control for Wireless Communication Systems

This dissertation is concerned with dynamical modeling, estimation and identification of wireless channels from received signal measurements. Optimal power control algorithms, mobile location and velocity estimation methods are developed based on the proposed models. The ultimate performance limits of any communication system are determined by the channel it operates in. In this dissertation, we propose new stochastic wireless channel models which capture both the space and time variations of wireless systems. The proposed channel models are based on stochastic differential equations (SDEs) driven by Brownian motions. These models are more realistic than the time invariant models encountered in the literature which do not capture and track the time varying characteristics of the propagation environment. The statistics of the proposed models are shown to be time varying, and converge in steady state to their static counterparts. Cellular and ad hoc wireless channel models are developed. In urban propagation environment, the parameters of the channel models can be determined from approximating the band-limited Doppler power spectral density (DPSD) by rational transfer functions. However, since the DPSD is not available on-line, a filterbased expectation maximization algorithm and Kalman filter to estimate the channel parameters and states, respectively, are proposed. The algorithm is recursive allowing the inphase and quadrature components and parameters to be estimated on-line from received signal measurements. The algorithms are tested using experimental data, and the results demonstrate the method’s viability for both cellular and ad hoc networks. Power control increases system capacity and quality of communications, and reduces battery power consumption. A stochastic power control algorithm is developed using the so-called predictable power control strategies. An iterative distributed algorithm is then deduced using stochastic approximations. The latter only requires each mobile to know its received signal to interference ratio at the receiver

Algebraic Statistics

Algebraic Statistics is concerned with the interplay of techniques from commutative algebra, combinatorics, (real) algebraic geometry, and related fields with problems arising in statistics and data science. This workshop was the first at Oberwolfach dedicated to this emerging subject area. The participants highlighted recent achievements in this field, explored exciting new applications, and mapped out future directions for research

Using the Sharp Operator for edge detection and nonlinear diffusion

In this paper we investigate the use of the sharp function known from functional analysis in image processing. The sharp function gives a measure of the variations of a function and can be used as an edge detector. We extend the classical notion of the sharp function for measuring anisotropic behaviour and give a fast anisotropic edge detection variant inspired by the sharp function. We show that these edge detection results are useful to steer isotropic and anisotropic nonlinear diffusion filters for image enhancement

Unexpected Improvements to Expected Improvement for Bayesian Optimization

Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often exceeded by that of more recent methods. Notably, EI and its variants, including for the parallel and multi-objective settings, are challenging to optimize because their acquisition values vanish numerically in many regions. This difficulty generally increases as the number of observations, dimensionality of the search space, or the number of constraints grow, resulting in performance that is inconsistent across the literature and most often sub-optimal. Herein, we propose LogEI, a new family of acquisition functions whose members either have identical or approximately equal optima as their canonical counterparts, but are substantially easier to optimize numerically. We demonstrate that numerical pathologies manifest themselves in "classic" analytic EI, Expected Hypervolume Improvement (EHVI), as well as their constrained, noisy, and parallel variants, and propose corresponding reformulations that remedy these pathologies. Our empirical results show that members of the LogEI family of acquisition functions substantially improve on the optimization performance of their canonical counterparts and surprisingly, are on par with or exceed the performance of recent state-of-the-art acquisition functions, highlighting the understated role of numerical optimization in the literature.Comment: NeurIPS 2023 Spotligh

Scalable 3D Surface Reconstruction by Local Stochastic Fusion of Disparity Maps

Digital three-dimensional (3D) models are of significant interest to many application fields, such as medicine, engineering, simulation, and entertainment. Manual creation of 3D models is extremely time-consuming and data acquisition, e.g., through laser sensors, is expensive. In contrast, images captured by cameras mean cheap acquisition and high availability. Significant progress in the field of computer vision already allows for automatic 3D reconstruction using images. Nevertheless, many problems still exist, particularly for big sets of large images. In addition to the complex formulation necessary to solve an ill-posed problem, one has to manage extremely large amounts of data. This thesis targets 3D surface reconstruction using image sets, especially for large-scale, but also for high-accuracy applications. To this end, a processing chain for dense scalable 3D surface reconstruction using large image sets is defined consisting of image registration, disparity estimation, disparity map fusion, and triangulation of point clouds. The main focus of this thesis lies on the fusion and filtering of disparity maps, obtained by Semi-Global Matching, to create accurate 3D point clouds. For unlimited scalability, a Divide and Conquer method is presented that allows for parallel processing of subspaces of the 3D reconstruction space. The method for fusing disparity maps employs local optimization of spatial data. By this means, it avoids complex fusion strategies when merging subspaces. Although the focus is on scalable reconstruction, a high surface quality is obtained by several extensions to state-of-the-art local optimization methods. To this end, the seminal local volumetric optimization method by Curless and Levoy (1996) is interpreted from a probabilistic perspective. From this perspective, the method is extended through Bayesian fusion of spatial measurements with Gaussian uncertainty. Additionally to the generation of an optimal surface, this probabilistic perspective allows for the estimation of surface probabilities. They are used for filtering outliers in 3D space by means of geometric consistency checks. A further improvement of the quality is obtained based on the analysis of the disparity uncertainty. To this end, Total Variation (TV)-based feature classes are defined that are highly correlated with the disparity uncertainty. The correlation function is learned from ground-truth data by means of an Expectation Maximization (EM) approach. Because of the consideration of a statistically estimated disparity error in a probabilistic framework for fusion of spatial data, this can be regarded as a stochastic fusion of disparity maps. In addition, the influence of image registration and polygonization for volumetric fusion is analyzed and used to extend the method. Finally, a multi-resolution strategy is presented that allows for the generation of surfaces from spatial data with a largely varying quality. This method extends state-of-the-art methods by considering the spatial uncertainty of 3D points from stereo data. The evaluation of several well-known and novel datasets demonstrates the potential of the scalable stochastic fusion method. The strength and the weakness of the method are discussed and direction for future research is given.Digitale dreidimensionale (3D) Modelle sind in vielen Anwendungsfeldern, wie Medizin, Ingenieurswesen, Simulation und Unterhaltung von signifikantem Interesse. Eine manuelle Erstellung von 3D-Modellen ist äußerst zeitaufwendig und die Erfassung der Daten, z.B. durch Lasersensoren, ist teuer. Kamerabilder ermöglichen hingegen preiswerte Aufnahmen und sind gut verfügbar. Der rasante Fortschritt im Forschungsfeld Computer Vision ermöglicht bereits eine automatische 3D-Rekonstruktion aus Bilddaten. Dennoch besteht weiterhin eine Vielzahl von Problemen, insbesondere bei der Verarbeitung von großen Mengen hochauflösender Bilder. Zusätzlich zur komplexen Formulierung, die zur Lösung eines schlecht gestellten Problems notwendig ist, besteht die Herausforderung darin, äußerst große Datenmengen zu verwalten. Diese Arbeit befasst sich mit dem Problem der 3D-Oberflächenrekonstruktion aus Bilddaten, insbesondere für sehr große Modelle, aber auch Anwendungen mit hohem Genauigkeitsanforderungen. Zu diesem Zweck wird eine Prozesskette zur dichten skalierbaren 3D-Oberflächenrekonstruktion für große Bildmengen definiert, bestehend aus Bildregistrierung, Disparitätsschätzung, Fusion von Disparitätskarten und Triangulation von Punktwolken. Der Schwerpunkt dieser Arbeit liegt auf der Fusion und Filterung von durch Semi-Global Matching generierten Disparitätskarten zur Bestimmung von genauen 3D-Punktwolken. Für eine unbegrenzte Skalierbarkeit wird eine Divide and Conquer Methode vorgestellt, welche eine parallele Verarbeitung von Teilräumen des 3D-Rekonstruktionsraums ermöglicht. Die Methode zur Fusion von Disparitätskarten basiert auf lokaler Optimierung von 3D Daten. Damit kann eine komplizierte Fusionsstrategie für die Unterräume vermieden werden. Obwohl der Fokus auf der skalierbaren Rekonstruktion liegt, wird eine hohe Oberflächenqualität durch mehrere Erweiterungen von lokalen Optimierungsmodellen erzielt, die dem Stand der Forschung entsprechen. Dazu wird die wegweisende lokale volumetrische Optimierungsmethode von Curless and Levoy (1996) aus einer probabilistischen Perspektive interpretiert. Aus dieser Perspektive wird die Methode durch eine Bayes Fusion von räumlichen Messungen mit Gaußscher Unsicherheit erweitert. Zusätzlich zur Bestimmung einer optimalen Oberfläche ermöglicht diese probabilistische Fusion die Extraktion von Oberflächenwahrscheinlichkeiten. Diese werden wiederum zur Filterung von Ausreißern mittels geometrischer Konsistenzprüfungen im 3D-Raum verwendet. Eine weitere Verbesserung der Qualität wird basierend auf der Analyse der Disparitätsunsicherheit erzielt. Dazu werden Gesamtvariation-basierte Merkmalsklassen definiert, welche stark mit der Disparitätsunsicherheit korrelieren. Die Korrelationsfunktion wird aus ground-truth Daten mittels eines Expectation Maximization (EM) Ansatzes gelernt. Aufgrund der Berücksichtigung eines statistisch geschätzten Disparitätsfehlers in einem probabilistischem Grundgerüst für die Fusion von räumlichen Daten, kann dies als eine stochastische Fusion von Disparitätskarten betrachtet werden. Außerdem wird der Einfluss der Bildregistrierung und Polygonisierung auf die volumetrische Fusion analysiert und verwendet, um die Methode zu erweitern. Schließlich wird eine Multi-Resolution Strategie präsentiert, welche die Generierung von Oberflächen aus räumlichen Daten mit unterschiedlichster Qualität ermöglicht. Diese Methode erweitert Methoden, die den Stand der Forschung darstellen, durch die Berücksichtigung der räumlichen Unsicherheit von 3D-Punkten aus Stereo Daten. Die Evaluierung von mehreren bekannten und neuen Datensätzen zeigt das Potential der skalierbaren stochastischen Fusionsmethode auf. Stärken und Schwächen der Methode werden diskutiert und es wird eine Empfehlung für zukünftige Forschung gegeben

AN ADAPTIVE LOCALIZATION SYSTEM USING PARTICLE SWARM OPTIMIZATION IN A CIRCULAR DISTRIBUTION FORM

Tracking the user location in indoor environment becomes substantial issue in recent research High accuracy and fast convergence are very important issues for a good localization system. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. In this paper, we introduce an algorithm using PSO for indoor localization system. The proposed algorithm uses PSO to generate several particles that have circular distribution around one access point (AP). The PSO generates particles where the distance from each particle to the AP is the same distance from the AP to the target. The particle which achieves correct distances (distances from each AP to target) is selected as the target. Four PSO variants, namely standard PSO (SPSO), linearly decreasing inertia weight PSO (LDIW PSO), self-organizing hierarchical PSO with time acceleration coefficients (HPSO-TVAC), and constriction factor PSO (CFPSO) are used to find the minimum distance error. The simulation results show the proposed method using HPSO-TVAC variant achieves very low distance error of 0.19 mete