4D Flow Visualization with Dynamic Vision Sensors

Borer, David

doi:10.3929/ethz-a-010344783

Cited by 4 publications

(1 citation statement)

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“…The advantage of this method is that, unlike conventional cameras, they measure intensity over a longer period of time and record changes of even single pixels [12,13]. Event-based sensors, apart from detecting the area of machine operation, are also used in human-machine interactions [14][15][16] or during fluid mechanics experiments [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Event Visualization and Trajectory Tracking of the Load Carried by Rotary Crane

Cekus

Depta

Kubanek

et al. 2022

Sensors

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Tracking the trajectory of the load carried by the rotary crane is an important problem that allows reducing the possibility of its damage by hitting an obstacle in its working area. On the basis of the trajectory, it is also possible to determine an appropriate control system that would allow for the safe transport of the load. This work concerns research on the load motion carried by a rotary crane. For this purpose, the laboratory crane model was designed in Solidworks software, and numerical simulations were made using the Motion module. The developed laboratory model is a scaled equivalent of the real Liebherr LTM 1020 object. The crane control included two movements: changing the inclination angle of the crane’s boom and rotation of the jib with the platform. On the basis of the developed model, a test stand was built, which allowed for the verification of numerical results. Event visualization and trajectory tracking were made using a dynamic vision sensor (DVS) and the Tracker program. Based on the obtained experimental results, the developed numerical model was verified. The proposed trajectory tracking method can be used to develop a control system to prevent collisions during the crane’s duty cycle.

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Section: Introductionmentioning

confidence: 99%

Event Visualization and Trajectory Tracking of the Load Carried by Rotary Crane

Cekus

Depta

Kubanek

et al. 2022

Sensors

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TrackAER: real-time event-based quantitative flow visualization

Rusch

Rösgen

2023

Exp Fluids

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We present a novel event-based quantitative flow visualization system, TrackAER, capable of continuously reconstructing, rendering and recording particle tracks in large test volumes without limitations on the measurement duration. Multiple event-based cameras are synchronized and calibrated to produce independent and asynchronous, yet temporally co-registered data streams of flow tracer positions. Subsequently, these data streams are merged into time-resolved three-dimensional particle tracks using photogrammetric techniques. Due to the operating principle of event cameras, the flow scenery is reduced to moving objects only, which effectively compresses the data stream at the camera source. In combination with an efficient data processing pipeline, the measurement system operates in real-time, reconstructing and rendering the investigated flow field without noticeable time lag. The data processing approach follows a “per-event” paradigm and enables the immediate observation and analysis of both, transient and long duration flow features. Specific issues resulting from event-based, frame-free processing are discussed as well as the advantages and limitations of event cameras. Exemplary results are provided to demonstrate the utility of the TrackAER system where Lagrangian particle track information is displayed in a virtual scene together with extracted quantitative information such as local flow velocities.

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TrackAER: Real-Time Event-Based Particle Tracking

Rusch

Roesgen

2021

ISPIV21

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Event-based cameras (Lichtsteiner et al., 2008; Posch et al., 2010; Gallego et al., 2020) operate fundamentally different from frame-based cameras: Each pixel of the sensor array reacts asynchronously to relative brightness changes creating a sequential stream of events in address-event representation (AER). Each event is defined by a microsecond-accurate time stamp, the pixel position and a binary polarity indicating a relative increase or decrease of light intensity. Thus, event-based cameras only sense changes in a scenery while effectively suppressing static, redundant information. This renders the camera technology promising also for flow diagnostics. In established approaches like PIV or PTV vast amounts of data are generated, only for a large part of redundant information to be eliminated in data post-processing. In contrast, eventbased cameras effectively compress the data stream already at the source. To make full use of this potential, new data processing algorithms are needed since event-based cameras do not generate conventional framebased data. This work utilizes an event-based camera to identify and track flow tracers such as helium-filled soap bubbles (HFSBs) with real-time visual feedback in measurement volumes of the order of several cubic meters.

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4D Flow Visualization with Dynamic Vision Sensors

Cited by 4 publications

References 0 publications

Event Visualization and Trajectory Tracking of the Load Carried by Rotary Crane

Event Visualization and Trajectory Tracking of the Load Carried by Rotary Crane

TrackAER: real-time event-based quantitative flow visualization

TrackAER: Real-Time Event-Based Particle Tracking

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