6DoF Pose-Estimation Pipeline for Texture-less Industrial Components in Bin Picking Applications

Blank, Andreas; Hiller, Manfred; Zhang, Siyi; Leser, Alexander; Metzner, Maximilian; Lieret, Markus; Thielecke, Jörn; Franke, Jörg

doi:10.1109/ecmr.2019.8870920

Cited by 19 publications

(1 citation statement)

References 28 publications

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“…Machine learning approaches are often considered as state of the art for object segmentation tasks. There are various neural networks available for object segmentation such as Mask R-CNN, DeepLab or FuseNet that allow an instance segmentation based on colour or combined colour and depth information [10][11][12] as well as advanced pose estimation pipelines for segmenting and grasping various objects [13]. Light-weight models and neural network architectures also allow a near real-time image analysis low-cost hardware such as the NVidia Jetson Nano Board or different single board computers equipped with the Intel Neural Compute Stick.…”

Section: Object Localization and Trackingmentioning

confidence: 99%

Localization and grasping of small load carriers with autonomous unmanned aerial vehicles

Lieret¹,

Lallinger²,

Tauscher³

et al. 2020

Annals of Scientific Society for Assembly, Handling and Industrial Robotics

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The application of unmanned aerial vehicles (UAV) in the area of inspection, survey or urban logistics has become a rapidly developing research domain. While the feasibility of material transports with UAVs has already been shown in the scope of different projects, the payload is thereby usually transferred manually into the UAV's load handling device. A decisive factor for the economic usability of UAVs for aerial transportation, however, is a fully automated system including the autonomous recognition and pick-up of the cargo. We therefore present a solution for the automated detection, localization and grasping of small load carriers with UAVs. The system includes a specialized load handing device, a camera-based real-time tracking solution for small load carriers and a fusion of the global and relative position measurements to achieve the in-flight positioning accuracy required for the autonomous cargo pick-up.

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Section: Object Localization and Trackingmentioning

confidence: 99%

Localization and grasping of small load carriers with autonomous unmanned aerial vehicles

Lieret¹,

Lallinger²,

Tauscher³

et al. 2020

Annals of Scientific Society for Assembly, Handling and Industrial Robotics

Self Cite

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Effective Bin Picking Approach by Combining Deep Learning and Point Cloud Processing Techniques

Tellaeche

Pastor-López

Urquijo

et al. 2020

Lecture Notes in Computer Science

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Adaptive Motion Control Middleware for Teleoperation Based on Pose Tracking and Trajectory Planning

Blank

Karlidag

Zikeli

et al. 2022

Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021

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Concurrent with autonomous robots, teleoperation gains importance in industrial applications. This includes human–robot cooperation during complex or harmful operations and remote intervention. A key role in teleoperation is the ability to translate operator inputs to robot movements. Therefore, providing different motion control types is a decisive aspect due to the variety of tasks to be expected. For a wide range of use-cases, a high degree of interoperability to a variety of robot systems is required. In addition, the control input should support up-to-date Human Machine Interfaces. To address the existing challenges, we present a middleware for teleoperation of industrial robots, which is adaptive regarding motion control types. Thereby the middleware relies on an open-source, robot meta-operating system and a standardized communication. Evaluation is performed within defined tasks utilizing different articulated robots, whereby performance and determinacy are quantified. An implementation sample of the method is available on: https://github.com/FAU-FAPS/adaptive_motion_control.

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6DoF Pose-Estimation Pipeline for Texture-less Industrial Components in Bin Picking Applications

Cited by 19 publications

References 28 publications

Localization and grasping of small load carriers with autonomous unmanned aerial vehicles

Localization and grasping of small load carriers with autonomous unmanned aerial vehicles

Effective Bin Picking Approach by Combining Deep Learning and Point Cloud Processing Techniques

Adaptive Motion Control Middleware for Teleoperation Based on Pose Tracking and Trajectory Planning

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