Tightly-coupled Fusion of Global Positional Measurements in Optimization-based Visual-Inertial Odometry

Motivated by the goal of achieving robust, drift-free pose estimation in long-term autonomous navigation, in this work we propose a methodology to fuse global positional information with visual and inertial measurements in a tightly-coupled nonlinear-optimization-based estimator. Differently from previous works, which are loosely-coupled, the use of a tightly-coupled approach allows exploiting the correlations amongst all the measurements. A sliding window of the most recent system states is estimated by minimizing a cost function that includes visual re-projection errors, relative inertial errors, and global positional residuals. We use IMU preintegration to formulate the inertial residuals and leverage the outcome of such algorithm to efficiently compute the global position residuals. The experimental results show that the proposed method achieves accurate and globally consistent estimates, with negligible increase of the optimization computational cost. Our method consistently outperforms the loosely-coupled fusion approach. The mean position error is reduced up to 50% with respect to the loosely-coupled approach in outdoor Unmanned Aerial Vehicle (UAV) flights, where the global position information is given by noisy GPS measurements. To the best of our knowledge, this is the first work where global positional measurements are tightly fused in an optimization-based visual-inertial odometry algorithm, leveraging the IMU preintegration method to define the global positional factors

HDVIO: Improving Localization and Disturbance Estimation with Hybrid Dynamics VIO

Visual-inertial odometry (VIO) is the most common approach for estimating the state of autonomous micro aerial vehicles using only onboard sensors. Existing methods improve VIO performance by including a dynamics model in the estimation pipeline. However, such methods degrade in the presence of low-fidelity vehicle models and continuous external disturbances, such as wind. Our proposed method, HDVIO, overcomes these limitations by using a hybrid dynamics model that combines a point-mass vehicle model with a learning-based component that captures complex aerodynamic effects. HDVIO estimates the external force and the full robot state by leveraging the discrepancy between the actual motion and the predicted motion of the hybrid dynamics model. Our hybrid dynamics model uses a history of thrust and IMU measurements to predict the vehicle dynamics. To demonstrate the performance of our method, we present results on both public and novel drone dynamics datasets and show real-world experiments of a quadrotor flying in strong winds up to 25 km/h. The results show that our approach improves the motion and external force estimation compared to the state-of-the-art by up to 33% and 40%, respectively. Furthermore, differently from existing methods, we show that it is possible to predict the vehicle dynamics accurately while having no explicit knowledge of its full state

A Three-Dimensional Numerical Study of Wave Induced Currents in the Cetraro Harbour Coastal Area (Italy)

In this paper we propose a three-dimensional numerical study of the coastal currents produced by the wave motion in the area opposite the Cetraro harbour (Italy), during the most significant wave event for the coastal sediment transport. The aim of the present study is the characterization of the current patterns responsible for the siltation that affects the harbour entrance area and the assessment of a project solution designed to limit this phenomenon. The numerical simulations are carried out by a three-dimensional non-hydrostatic model that is based on the Navier–Stokes equations expressed in integral and contravariant form on a time-dependent curvilinear coordinate system, in which the vertical coordinate moves in order to follow the free surface variations. The numerical simulations are carried out in two different geometric configurations: a present configuration, that reproduces the geometry of the coastal defence structures currently present in the harbour area and a project configuration, which reproduces the presence of a breakwater designed to modify the coastal currents in the area opposite the harbour entrance

Learned Inertial Odometry for Autonomous Drone Racing

Inertial odometry is an attractive solution to the problem of state estimation for agile quadrotor flight. It is inexpensive, lightweight, and it is not affected by perceptual degradation. However, only relying on the integration of the inertial measurements for state estimation is infeasible. The errors and time-varying biases present in such measurements cause the accumulation of large drift in the pose estimates. Recently, inertial odometry has made significant progress in estimating the motion of pedestrians. State-of-the-art algorithms rely on learning a motion prior that is typical of humans but cannot be transferred to drones. In this work, we propose a learning-based odometry algorithm that uses an inertial measurement unit (IMU) as the only sensor modality for autonomous drone racing tasks. The core idea of our system is to couple a model-based filter, driven by the inertial measurements, with a learning-based module that has access to the control commands. We show that our inertial odometry algorithm is superior to the state-of-the-art filter-based and optimization-based visual- inertial odometry as well as the state-of-the-art learned-inertial odometry. Additionally, we show that our system is comparable to a visual-inertial odometry solution that uses a camera and exploits the known gate location and appearance. We believe that the application in autonomous drone racing paves the way for novel research in inertial odometry for agile quadrotor flight. We will release the code upon acceptance

Powerline Tracking with Event Cameras

Autonomous inspection of powerlines with quadrotors is challenging. Flights require persistent perception to keep a close look at the lines. We propose a method that uses event cameras to robustly track powerlines. Event cameras are inherently robust to motion blur, have low latency, and high dynamic range. Such properties are advantageous for autonomous inspection of powerlines with drones, where fast motions and challenging illumination conditions are ordinary. Our method identifies lines in the stream of events by detecting planes in the spatio-temporal signal, and tracks them through time. The implementation runs onboard and is capable of detecting multiple distinct lines in real time with rates of up to 320 thousand events per second. The performance is evaluated in real-world flights along a powerline. The tracker is able to persistently track the powerlines, with a mean lifetime of the line 10× longer than existing approaches

Left ventricular systolic dysfunction in chronic kidney disease: from asymptomatic changes in geometry and function to overt heart failure.

A bidirectional relationship between kidney and heart function is present in all stages of cardiac and renal disease, from the asymptomatic phase of left ventricular systolic dysfunction to overt heart failure, as well as from the initial reduction of glomerular filtration rate to end-stage kidney disease, respectively. The simultaneous presence of both diseases has a significant impact on prognosis and requires specific therapeutic strategies. The early recognition of abnormalities of renal and myocardial function may have a relevant influence on management of combination of these conditions

MULTI-AGENT INFRASTRUCTURES FOR OBJECTIVE AND SUBJECTIVE COORDINATION

Coordination in MAS can be conceived as either an agent activity (the subjective viewpoint) or an activity over agents (the objective viewpoint). The two viewpoints have generated two diverging and often contrasting lines of research, as well as different and non-compatible technologies: however, their integration is mandatory for modelling and engineering complex MAS. In this paper, we explore the issue of integration at both the model and the technology levels. First, by taking FIPA agents and coordination artifacts as reference notions for subjective and objective approaches, respectively, we sketch a framework where agent interactions with coordination artifacts are modelled as physical acts, deliberated and executed by agents analogously to communicative actions. Then, we show how the JADE infrastructure for FIPA-compliant agents, and the TuCSoN infrastructure providing agents with coordination artifacts can be integrated at the technology level, allowing JADE agents to access TuCSoN tuple centres through JADE services

Sex-Specific Association of Left Ventricular Hypertrophy With Rheumatoid Arthritis

Objectives: Clinical expression of rheumatoid arthritis (RA) varies by gender, but whether cardiovascular disease (CVD) is gender related in RA is unknown. Left ventricular (LV) hypertrophy (LVH) is a hallmark of CVD in RA patients. We investigated whether the association of LVH with RA is gender driven.Methods: Consecutive outpatients with established RA underwent echocardiography with measurement of LVH at baseline and one follow-up. All participants had no prior history of CVD or diabetes mellitus. We assessed CVD risk factors associated with LVH at follow-up, including sex, age, arterial blood pressure, and body mass index (BMI). We also evaluated inflammatory markers, autoimmunity, disease activity, and the use of RA medications as predictors of LVH.Results: We recruited 145 RA patients (121 females, 83%) and reassessed them after a median (interquartile range) of 36 months (24–50). At baseline, women were more dyslipidemic but otherwise had fewer CVD risk factors than men, including less prevalent smoking habit and hypertension, and smaller waist circumference. At follow-up, we detected LVH in 42/145 (44%) RA patients. LV mass significantly increased only in women. In multiple Cox regression analysis, women with RA had the strongest association with LVH, independently from the presence of CVD risk factors (OR, 6.56; 95% CI, 1.34–30.96) or RA-specific characteristics (OR, 5.14; 95% CI, 1.24–21.34). BMI was also significantly and independently associated with LVH.Conclusion: Among established RA patients, women carry the highest predisposition for LVH