On-manifold Decentralized State Estimation using Pseudomeasurements and Preintegration

This paper addresses the problem of decentralized, collaborative state estimation in robotic teams. In particular, this paper considers problems where individual robots estimate similar physical quantities, such as each other's position relative to themselves. The use of \emph{pseudomeasurements} is introduced as a means of modelling such relationships between robots' state estimates, and is shown to be a tractable way to approach the decentralized state estimation problem. Moreover, this formulation easily leads to a general-purpose observability test that simultaneously accounts for measurements that robots collect from their own sensors, as well as the communication structure within the team. Finally, input preintegration is proposed as a communication-efficient way of sharing odometry information between robots, and the entire theory is appropriate for both vector-space and Lie-group state definitions. The proposed framework is evaluated on three different simulated problems, and one experiment involving three quadcopters.Comment: 15 pages, 13 figures, submitted to IEE

Reducing Two-Way Ranging Variance by Signal-Timing Optimization

Time-of-flight-based range measurements among transceivers with different clocks requires ranging protocols that accommodate for the varying rates of the clocks. Double-sided two-way ranging (DS-TWR) has recently been widely adopted as a standard protocol due to its accuracy; however, the precision of DS-TWR has not been clearly addressed. In this paper, an analytical model of the variance of DS-TWR is derived as a function of the user-programmed response delays. Consequently, this allows formulating an optimization problem over the response delays in order to maximize the information gained from range measurements by addressing the effect of varying the response delays on the precision and frequency of the measurements. The derived analytical variance model and proposed optimization formulation are validated experimentally with 2 ranging UWB transceivers, where 29 million range measurements are collected.Comment: 5 pages, 4 figures, submitted to 2023 International Conference on Acoustics, Speech and Signal Processing (ICASSP

navlie: A Python Package for State Estimation on Lie Groups

The ability to rapidly test a variety of algorithms for an arbitrary state estimation task is valuable in the prototyping phase of navigation systems. Lie group theory is now mainstream in the robotics community, and hence estimation prototyping tools should allow state definitions that belong to manifolds. A new package, called navlie, provides a framework that allows a user to model a large class of problems by implementing a set of classes complying with a generic interface. Once accomplished, navlie provides a variety of on-manifold estimation algorithms that can run directly on these classes. The package also provides a built-in library of common models, as well as many useful utilities. The open-source project can be found at https://github.com/decargroup/navlie.Comment: 6 pages, 8 figures, presented at the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Novel Approaches For Segmenting Cerebral Vasculature

In this chapter, we propose two segmentation approaches that are able to segment cerebral vasculature automatically and accurately. This would potentially help experts in the early analysis and diagnosis of severe diseases, specifically, multiple sclerosis

A Noninvasive Image-Based Approach Toward An Early Diagnosis Of Autism

The ultimate goal of this chapter is to develop a computer-aided diagnostic system for the accurate and early diagnosis of autism spectrum disorder using diffusion tensor imaging. This system consists of three main steps. First, the brain tissues are segmented based on three image descriptors. Second, discriminatory features are extracted from the segmented brains. Finally, the diagnostic capabilities of these extracted features are investigated

Computational Analysis Techniques: A Case Study On Fmri For Autism Spectrum Disorder

Functional magnetic resonance imaging (MRI) is one of the most promising techniques in neuro-imaging. It is mainly used to either record the response of a subject to a certain task (task based fMRI) or to assess the functional connectivity while at rest (resting state fMRI). In this survey, both fMRI techniques are explained and the most commonly used techniques in each of them are discussed and criticized. For each technique the hypothesis used and the mathematical background is discussed

10 A noninvasive approach for the early detection of diabetic retinopathy

This chapter introduces one of the most critical problems in ophthalmology, specifically the diagnosis and detection of diabetic retinopathy (DR). Developing a fast, accurate, and reliable method for the early detection of DR is of great clinical importance to prevent blindness in patients. For this reason, various methods for early detection of DR have been investigated and used such as a dilated eye examination, tonometry, fluorescein angiography, optical coherence tomography, and ultrawide-field retinal imaging. With the increased popularity of machine learning, researchers have formulated their own algorithms and methods to detect DR with various rates of success. This chapter overviews past and current diagnostic methods that have been developed for DR. In addition, this chapter addresses new methodologies being developed/researched and some challenges that researchers face in developing fast, accurate, and reliable diagnosis

Towards A Robust Cad System For Early Diagnosis Of Autism Using Structural Mri

This chapter discusses a promising computer-aided diagnosis system, devised by our research team, for diagnosing autism at various stages of life, making use of the shape information in brain magnetic resonance imaging. Our system integrates the shape features extracted from both the cerebral white matter and the cerebral cortex

Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging

© 2013 IEEE. Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using the Johns Hopkins WM atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support