The Event-Driven Software Library for YARP—With Algorithms and iCub Applications

Glover, Arren; Vasco, Valentina; Iacono, Massimiliano; Bartolozzi, Chiara

doi:10.3389/frobt.2017.00073

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…3) and software libraries for handling events. Open source JAVA 31 and C++ 32,33 libraries have already been developed, also within two of the main robotic middlewares -ROS and YARPbut they require additional contributions from a large community to grow and reach the maturity needed for successful adoption in robotics. Eventually, a hybrid approach that combines frame-based and event-driven modules, and that fosters the growth of the community revolving around it, could favour a more widespread adoption in the robotics domain.…”

Section: Box 1 | the Need For Adaptation In Roboticsmentioning

confidence: 99%

Embodied neuromorphic intelligence

2022

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The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies – from perception to motor control – represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations.

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Section: Box 1 | the Need For Adaptation In Roboticsmentioning

confidence: 99%

Embodied neuromorphic intelligence

2022

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“…of the image point u, where ũ = [u T , 1] T and K (1:2) and K (3) are respectively the first-2 rows and 3rd row of the camera intrinsic matrix K ∈ R 3×3 [22] (similarly for u ).…”

Section: Event-based Relative Motion Estimationmentioning

confidence: 99%

Spatiotemporal Registration for Event-based Visual Odometry

Liu¹,

Parra²,

Chin³

2021

Preprint

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A useful application of event sensing is visual odometry, especially in settings that require high-temporal resolution. The state-of-the-art method of contrast maximisation recovers the motion from a batch of events by maximising the contrast of the image of warped events. However, the cost scales with image resolution and the temporal resolution can be limited by the need for large batch sizes to yield sufficient structure in the contrast image 1 . In this work, we propose spatiotemporal registration as a compelling technique for event-based rotational motion estimation. We theoretically justify the approach and establish its fundamental and practical advantages over contrast maximisation. In particular, spatiotemporal registration also produces feature tracks as a by-product, which directly supports an efficient visual odometry pipeline with graph-based optimisation for motion averaging. The simplicity of our visual odometry pipeline allows it to process more than 1 M events/second. We also contribute a new event dataset for visual odometry, where motion sequences with large velocity variations were acquired using a high-precision robot arm 2 .

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“…• The event-driven YARP Project 14 [270] comprises libraries to handle neuromorphic sensors, such as the DVS, installed on the iCub humanoid robot, along with algorithms to process event data. It is based on the Yet Another Robot Platform (YARP) middleware.…”

Section: Softwarementioning

confidence: 99%

Event-based Vision: A Survey

Gallego,

Delbruck,

Orchard

et al. 2019

Preprint

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Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of µs), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.

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The Event-Driven Software Library for YARP—With Algorithms and iCub Applications

Cited by 18 publications

References 20 publications

Embodied neuromorphic intelligence

Embodied neuromorphic intelligence

Spatiotemporal Registration for Event-based Visual Odometry

Event-based Vision: A Survey

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