Myocardial Motion Analysis for Determination of Tei-Index of Human Heart

The Tei index, an important indicator of heart function, lacks a direct method to compute because it is difficult to directly evaluate the isovolumic contraction time (ICT) and isovolumic relaxation time (IRT) from which the Tei index can be obtained. In this paper, based on the proposed method of accurately measuring the cardiac cycle physical phase, a direct method of calculating the Tei index is presented. The experiments based on real heart medical images show the effectiveness of this method. Moreover, a new method of calculating left ventricular wall motion amplitude is proposed and the experiments show its satisfactory performance

Real-Time Long Range Trajectory Replanning for MAVs in the Presence of Dynamic Obstacles

Real-time long-range local planning is a challenging task, especially in the presence of dynamics obstacles. We propose a complete system which is capable of performing the local replanning in real-time. Desired trajectory is needed in the system initialization phase; system starts initializing sub-components of the system including point cloud processor, trajectory estimator and planner. Afterwards, the multi-rotary aerial vehicle starts moving on the given trajectory. When it detects obstacles, it replans the trajectory from the current pose to pre-defined distance incorporating the desired trajectory. Point cloud processor is employed to identify the closest obstacles around the vehicle. For replanning, Rapidly-exploring Random Trees (RRT*) is used with two modifications which allow planning the trajectory in milliseconds scales. Once we replanned the desired path, velocity components(x,y and z) and yaw rate are calculated. Those values are sent to the controller at a constant frequency to maneuver the vehicle autonomously. Finally, we have evaluated each of the components separately and tested the complete system in the simulated and real environments

Continuous-Time Ultra-Wideband-Inertial Fusion

We present a novel continuous-time online state estimation framework using ultra-wideband and inertial sensors. For representing motion states continuously over time, quaternion-based cubic B-splines are exploited with efficient solutions to kinematic interpolations and spatial differentiations. Based thereon, a sliding-window spline fitting scheme is established for asynchronous multi-sensor fusion and online calibration. We evaluate the proposed system, SFUISE (spline fusion-based ultra-wideband-inertial state estimation), in real-world scenarios based on public data set and experiments. The proposed spline fusion scheme is real-time capable and delivers superior performance over state-of-the-art discrete-time schemes. We release the source code and own experimental data set at https://github.com/KIT-ISAS/SFUISE.Comment: 8 pages, submitted to IEEE Robotics and Automation Letters (RA-L

Jacobian Computation for Cumulative B-Splines on SE(3) and Application to Continuous-Time Object Tracking

In this paper we propose a method that estimates the SE(3) continuous trajectories (orientation and translation) of the dynamic rigid objects present in a scene, from multiple RGB-D views. Specifically, we fit the object trajectories to cumulative B-Splines curves, which allow us to interpolate, at any intermediate time stamp, not only their poses but also their linear and angular velocities and accelerations. Additionally, we derive in this work the analytical SE(3) Jacobians needed by the optimization, being applicable to any other approach that uses this type of curves. To the best of our knowledge this is the first work that proposes 6-DoF continuous-time object tracking, which we endorse with significant computational cost reduction thanks to our analytical derivations. We evaluate our proposal in synthetic data and in a public benchmark, showing competitive results in localization and significant improvements in velocity estimation in comparison to discrete-time approaches. © 2016 IEEE

Continuous-Time Vs. Discrete-Time Vision-Based SLAM: A Comparative Study

Robotic practitioners generally approach the vision-based SLAM problem through discrete-time formulations. This has the advantage of a consolidated theory and very good understanding of success and failure cases. However, discrete-time SLAM needs tailored algorithms and simplifying assumptions when high-rate and/or asynchronous measurements, coming from different sensors, are present in the estimation process. Conversely, continuous-time SLAM, often overlooked by practitioners, does not suffer from these limitations. Indeed, it allows integrating new sensor data asynchronously without adding a new optimization variable for each new measurement. In this way, the integration of asynchronous or continuous high-rate streams of sensor data does not require tailored and highly-engineered algorithms, enabling the fusion of multiple sensor modalities in an intuitive fashion. On the down side, continuous time introduces a prior that could worsen the trajectory estimates in some unfavorable situations. In this work, we aim at systematically comparing the advantages and limitations of the two formulations in vision-based SLAM. To do so, we perform an extensive experimental analysis, varying robot type, speed of motion, and sensor modalities. Our experimental analysis suggests that, independently of the trajectory type, continuous-time SLAM is superior to its discrete counterpart whenever the sensors are not time-synchronized. In the context of this work, we developed, and open source, a modular and efficient software architecture containing state-of-the-art algorithms to solve the SLAM problem in discrete and continuous time

Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization

We focus on a replanning scenario for quadrotors where considering time efficiency, non-static initial state and dynamical feasibility is of great significance. We propose a real-time B-spline based kinodynamic (RBK) search algorithm, which transforms a position-only shortest path search (such as A* and Dijkstra) into an efficient kinodynamic search, by exploring the properties of B-spline parameterization. The RBK search is greedy and produces a dynamically feasible time-parameterized trajectory efficiently, which facilitates non-static initial state of the quadrotor. To cope with the limitation of the greedy search and the discretization induced by a grid structure, we adopt an elastic optimization (EO) approach as a post-optimization process, to refine the control point placement provided by the RBK search. The EO approach finds the optimal control point placement inside an expanded elastic tube which represents the free space, by solving a Quadratically Constrained Quadratic Programming (QCQP) problem. We design a receding horizon replanner based on the local control property of B-spline. A systematic comparison of our method against two state-of-the-art methods is provided. We integrate our replanning system with a monocular vision-based quadrotor and validate our performance onboard.Comment: 8 pages. Published in International Conference on Robotics and Automation (ICRA) 2018. IEEE copyrigh