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Oesophageal Electrode Probe and Device for Cardiological Treatment and/or Diagnosis (US20200261024)
(2020)
An oesophageal electrode probe for bioimpedance measurement and/or for neurostimulation is provided; a device for transoesophageal cardiological treatment and/or cardiological diagnosis is also provided; a method for the open-loop or closed-loop control of a cardiological catheter ablation device and/or a cardiological, circulatory and/or respiratory support device is also provided. The oesophageal electrode probe comprises a bioimpedance measuring device for measuring the bioimpedance of at least one part of tissue surrounding the oesophageal electrode probe. The bioimpedance device comprises at least one first and one second electrode. The at least one first electrode is arranged on a side of the oesophageal electrode probe facing towards the heart. The at least one second electrode is arranged on a side of the oesophageal electrode probe facing away from the heart. The device comprises the oesophageal electrode probe and a control and/or evaluation device.
本发明涉及一种用于生物阻抗测量和/或用于神经刺激的食道电极探针(10);用于经食道心脏病治疗和/或心脏病诊断的设备(100);以及一种用于控制或调节用于心脏导管消融装置和/或心脏、循环和/或肺支持装置的方法。食道电极探针包括生物阻抗测量装置,用于测量围绕食道电极探针的组织中的至少一部分组织的生物阻抗。生物阻抗装置包括至少一个第一电极和至少一个第二电极,其中至少一个第一电极(12A)布置在食道电极探针的面向心脏的一侧(14)上,并且至少一个第二电极(12B)布置在食道电极探针背离心脏的一侧(16)上。装置(100)包括食道电极探针(10)和控制和/或评估装置(30),其被配置用于从至少一个第一电极(12A)接收第一生物阻抗测量信号并从至少一个第二电极(12B)接收第二生物阻抗测量信号,并对这些信号进行比较,并且在比较的基础上产生控制信号。该控制信号可以是用于控制或调节心脏导管消融装置和/或心脏、循环和/或肺支持装置的信号。
The invention relates to an oesophageal electrode probe (10) for bioimpedance measurement and/or for neurostimulation; a device (100) for transoesophageal cardiological treatment and/or cardiological diagnosis; and a method for the open-loop or closed-loop control of a cardiac catheter ablation device and/or a cardiac, circulatory and/or respiratory support device. The oesophageal electrode probe comprises a bioimpedance measuring device for measuring the bioimpedance of at least one part of the tissue surrounding the oesophageal electrode probe. The bioimpedance device comprises at least one first and one second electrode, wherein the at least one first electrode (12A) is arranged on a side (14) of the oesophageal electrode probe facing towards the heart and the at least one second electrode (12B) is arranged on a side (16) of the oesophageal electrode probe facing away from the heart. The device (100) comprises the oesophageal electrode probe (10) and a control and/or evaluation device (30), which is configured for receiving a first bioimpedance measurement signal from the at least one first electrode (12A) and a second bioimpedance measurement signal from the at least one second electrode (12B), and comparing same, and generating a control signal on the basis of the comparison. The control signal can be a signal for the open-loop or closed-loop control of a cardiac catheter ablation device and/or a cardiac, circulatory and/or respiratory support device.
This study focuses on the autonomous navigation and mapping of indoor environments using a drone equipped only with a monocular camera and height measurement sensors. A visual SLAM algorithm was employed to generate a preliminary map of the environment and to determine the drone's position within the map. A deep neural network was utilized to generate a depth image from the monocular camera's input, which was subsequently transformed into a point cloud to be projected into the map. By aligning the depth point cloud with the map, 3D occupancy grid maps were constructed by using ray tracing techniques to get a precise depiction of obstacles and the surroundings. Due to the absence of IMU data from the low-cost drone for the SLAM algorithm, the created maps are inherently unscaled. However, preliminary tests with relative navigation in unscaled maps have revealed potential accuracy issues, which can only be overcome by incorporating additional information from the given sensors for scale estimation.
Modern industrial production is heavily dependent on efficient workflow processes and automation. The steady flow of raw materials as well as the separation of vital parts and semi-finished products are at the core of these automated procedures. Commonly used systems for this work are bowl feeders, which separate the parts and material by a combination of mechanical vibration and friction. The production of these tools, especially the design of the ramping spiral, is delicate and time-consuming work, as the shape, slope, and material must be carefully adjusted for the corresponding parts. In this work, we propose an automated approach, making use of optimization procedures from artificial intelligence, to design the spiral ramps of the bowl feeders. Therefore, the whole system and considered parts are physically simulated and the optimized geometry is subsequently exported into a CAD system for the actual building, respectively printing. The employment of evolutionary optimization gives the need to develop a mathematical model for the whole setup and find an efficient representation of integral features.
The precise positioning of mobile systems is a prerequisite for any autonomous behavior, in an industrial environment as well as for field robotics. The paper describes the set up for an experimental platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. Two approaches are compared. First, a local method based on point cloud matching and integration of inertial measurement units is evaluated. Subsequent matching makes it possible to create a three-dimensional point cloud that can be used as a map in subsequent runs. The second approach is a full SLAM algorithm, based on graph relaxation models, incorporating the full sensor suite of odometry, inertial sensors, and 3D laser scan data.
Die Positionierung mobiler Systeme mit hoher Genauigkeit ist eine Voraussetzung für intelligentes autonomes Verhalten, sowohl in der Feldrobotik als auch in industriellen Umgebungen. Dieser Beitrag beschreibt den Aufbau einer Roboterplattform und ihre Verwendung für den Test und die Bewertung von Kalman-Filter-Konfigurationen. Der Aufbau wurde mit einem mobilen Roboter Husky A200 und einem LiDAR-Sensor (Light Detection and Ranging) realisiert. Zur Verifizierung des vorgeschlagenen Aufbaus wurden fünf verschiedene Szenarien ausgearbeitet. Mit denen wurden die Filter auf ihre Leistungsfähigkeit hinsichtlich der Genauigkeit der Positionsbestimmung getestet.
Evaluierung von Kalman Filter Konfigurationen zur Roboterlokaliserung mittels Sensordatenfusion
(2023)
In dieser Arbeit werden drei verschiedene Konfigurationen der von Tom Moore, für das Robot Operating System, entwickelte Kalman-Filter vorgestellt. Diese bilden die Grundlage für eine Lokalisierung mittels Sensorfusion in dem verwendeten ROS-Framework. Ziel dieser Arbeit ist der Aufbau und die Verifikation einer Lokalisierung für ein mobiles Robotersystem Husky A200 der Firma Clearpath Robotics. Hierzu wurden die Möglichkeiten des bestehenden Systems untersucht und mehrere Versionen von Lokalisierungsfiltern konfiguriert. Am an Ende, wird eine Verifikation der Ergebnisse in verschiedenen Szenarien gegeneinandergestellt. Hierzu werden die Ergebnisse einer Variante des Extended Kalman-Filters in 2D (EKF2D), eine Variante des Unscented Kalman-Filter in 2D (UKF2D) und eine Variante des Extended Kalman-Filters in 3D (EKF3D) verifiziert und verglichen. Die Untersuchungen ergaben das der EKF2D die besten und robustesten Ergebnisse für eine Lokalisierung erbringt, trotz, im Vergleich zu der UKF2D Variante, 17,3 % höhere Endpositionsabweichung aufweist. Die in diesem Projekt gewählte EKF3D Konfigurationsvariante eignet sich, wegen seinen starken Ungenauigkeiten in der Höhenbestimmung nicht für eine aussagekräftige Positionsbestimmung.
Mit der Implementierung sowie einer anschließenden aussagekräftigen Evaluierung, soll das, visuelle-inertiale Kartierungs- und Lokalisierungssystem maplab analysiert werden. Hierbei basiert die Kartierung bzw. Lokalisierung auf der Detektion von Umgebungsmerkmalen. Neben der Möglichkeit der Kartenerstellung besteht ferner die Option, mehrere Karten zu fusionieren und somit weitreichende Gebiete zu kartieren sowie für weitere Datenauswertungen zu nutzen. Aufgrund der Durchführung und Bewertung der Ergebnisse in unterschiedlichen Anwendungsszenarien zeigt sich, dass maplab besonders zur Kartierung von Räumen bzw. kleinen Gebäudekomplexen geeignet ist. Die Möglichkeit der Kartenfusionierung bietet weiterhin die Option, den Informationsgehalt von Karten zu erhöhen, welches die Effektivität für eine anschließende Lokalisierung steigert. Bei wachsender Kartierungsgröße hingegen zeigt sich jedoch eine Vergrößerung geometrischer Inkonsistenzen.
A novel approach for synchronization and calibration of a camera and an inertial measurement unit (IMU) in the research-oriented visual-inertial mapping-and localization-framework maplab is presented. Mapping and localization are based on detecting different features in the environment. In addition to the possibility of creating single-case maps, the included algorithms allow merging maps to increase mapping accuracy and obtain large-scale maps. Furthermore, the algorithms can be used to optimize the collected data. The preliminary results show that after appropriate calibration and synchronization maplab can be used efficiently for mapping, especially in rooms and small building environments.