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YARP integration for event-cameras and other neuromorphic sensors

Libraries that handle neuromorphic sensors, such as the dynamic vision sensor, installed on the iCub can be found here, along with algorithms to process the event-based data.

@article{Glover2017b,
author = {Glover, Arren and Vasco, Valentina and Iacono, Massimiliano and Bartolozzi, Chiara},
doi = {10.3389/frobt.2017.00073},
journal = {Frontiers in Robotics and AI},
pages = {73},
title = {{The event-driven Software Library for YARP — With Algorithms and iCub Applications}},
volume = {4},
year = {2018}
}

Event-driven libraries provide basic functionality for handling events in a YARP environment. The library has definitions for:

• core
  • codecs to encode/decode events to be compatible with address event representation (AER) formats.
  • Sending packets of events in ev::packet that is compatible with yarpdatadumper and yarpdataplayer.
  • asynchronous reading and writing ports that ensure data is never lost and giving access to latency information.
  • helper functions to handle event timestamp wrapping and to convert between timestamps and seconds.
• vision
  • filters for removing salt and pepper noise.
  • sparse event warping using camera intrinsic parameters and extrinsic parameters for a stereo-pair
  • methods to draw events onto the screen in a variety of methods
• algorithms
  • event surfaces such as the Surface of Active Events (SAE), Polarity Integrated Images (PIM), and Exponentially Reduced Ordinal Surface (EROS)
  • corner detection
  • optical flow

• vFramer - visualisation of events streamed over a YARP port. Various methods for visualisation are available.
• calibration - estimating the camera intrinsic parameters
• vPreProcess - splitting different event-types into separate event-streams, performing filtering, and simple augmentations (flipping etc.)
• atis-bridge - bridge between the Prophesee ATIS cameras and YARP
• x320-bridge - bridge from Prophesee x320 chip using KTH's STM32 USB forwarding
• zynqGrabber - bridge between zynq-based FPGA sensor interface and YARP
• event video creation - create nice 3D (x,y,t) videos from data files
• visualisation and annotation
• data format conversion

• EDOPT: 6-DoF Object Tracking
• MoveEnet: Human Pose Estimation
• Optical Flow
• Line Tracking

Comprehensive instructions available for installation.

Glover, A., and Bartolozzi C. (2016) Event-driven ball detection and gaze fixation in clutter. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2016, Daejeon, Korea. Finalist for RoboCup Best Paper Award

Glover, A., Gava, L., Li, Z., & Bartolozzi, C. (2024, May). Edopt: Event-camera 6-dof dynamic object pose tracking. In 2024 IEEE International Conference on Robotics and Automation (ICRA) (pp. 18200-18206). IEEE.

Vasco V., Glover A., and Bartolozzi C. (2016) Fast event-based harris corner detection exploiting the advantages of event-driven cameras. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2016, Daejeon, Korea.

V. Vasco, A. Glover, Y. Tirupachuri, F. Solari, M. Chessa, and Bartolozzi C. Vergence control with a neuromorphic iCub. In IEEE-RAS International Conference on Humanoid Robots (Humanoids), November 2016, Mexico.

Glover, A., & Bartolozzi, C. (2017, September). Robust visual tracking with a freely-moving event camera. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3769-3776). IEEE.

Iacono, M., Weber, S., Glover, A., & Bartolozzi, C. (2018, October). Towards event-driven object detection with off-the-shelf deep learning. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1-9). IEEE.

Vasco, V., Glover, A., Mueggler, E., Scaramuzza, D., Natale, L., & Bartolozzi, C. (2017, July). Independent motion detection with event-driven cameras. In 2017 18th International Conference on Advanced Robotics (ICAR) (pp. 530-536). IEEE.

Goyal, G., Di Pietro, F., Carissimi, N., Glover, A., & Bartolozzi, C. (2023, June). Moveenet: Online high-frequency human pose estimation with an event camera. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 4024-4033). IEEE.

Glover, A., Dinale, A., Rosa, L. D. S., Bamford, S., & Bartolozzi, C. (2021). luvharris: A practical corner detector for event-cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 10087-10098.

Kreiser, R., Renner, A., Leite, V. R., Serhan, B., Bartolozzi, C., Glover, A., & Sandamirskaya, Y. (2020). An on-chip spiking neural network for estimation of the head pose of the icub robot. Frontiers in Neuroscience, 14, 551.

Glover, A., Vasco, V., & Bartolozzi, C. (2018, May). A controlled-delay event camera framework for on-line robotics. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2178-2183). IEEE.

Iacono, M., D’Angelo, G., Glover, A., Tikhanoff, V., Niebur, E., & Bartolozzi, C. (2019, November). Proto-object based saliency for event-driven cameras. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 805-812). IEEE.

Gava, L., Monforte, M., Bartolozzi, C., & Glover, A. (2022, June). How late is too late? a preliminary event-based latency evaluation. In 2022 8th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP) (pp. 1-4). IEEE.\

Li, Z., Glover, A., Bartolozzi, C., & Natale, L. (2025, October). 6-DoF Object Tracking with Event-based Optical Flow and Frames. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 18880-18887). IEEE.