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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Goyal, Gaurvi Di Pietro, Franco Carissimi, Nicolo Glover, Arren Bartolozzi, Chiara Event-Driven Perception for Robotics Istituto Italiano di Tecnologia Italy

ISBN: (纸本)9798350302493

Human Pose Estimation (HPE) is crucial as a building block for tasks that are based on the accurate understanding of human position, pose and movements. Therefore, accuracy and efficiency in this block echo throughout a system, making it important to find efficient methods, that run at fast rates for online applications. The state of the art for mainstream sensors has made considerable advances, but event camera based HPE is still in its infancy. event cameras boast high rates of data capture in a compact data structure, with advantages like high dynamic range and low power consumption. In this work, we present a system for a high frequency estimation of 2D, single-person Human Pose with event cameras. We provide an online system, that can be paired directly with an event camera to obtain high accuracy in real time. For quantitative results, we present our results on two large scale datasets, DHP19 and event-Human 3.6m. The system is robust to variance in the resolution of the camera and can run at up to 100Hz and an accuracy 89%. © 2023 IEEE.

关键词： Cameras

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Fast Trajectory End-Point Prediction with event Cameras for Reactive Robot Control

Fast Trajectory End-Point Prediction with Event Cameras for ...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Monforte, Marco Gava, Luna Iacono, Massimiliano Glover, Arren Bartolozzi, Chiara Event-Driven Perception for Robotics Istituto Italiano di Tecnologia Italy

ISBN: (纸本)9798350302493

Prediction can be crucial for tasks with tight time constraints if a robot has limited speed and power. A low-latency, high-frequency perception system can reduce the time needed to converge on the expectation of the future state of the world, giving the robot additional time to act - or to choose a safer action. In this paper, we exploit event cameras for asynchronous motion-driven sampling, inherent data compression, and sub-millisecond latency to reduce the convergence time of a data-driven trajectory prediction algorithm. As a use-case, we use a Panda robotic arm to intercept a ball bouncing on a table. To predict the interception point as early as possible, and cope with the intrinsic variability of trajectory length - that cannot be defined a-priori for event cameras - we adopt a Stateful Long Short-Term Memory network, that asynchronously updates the prediction for each incoming point of the trajectory and does not require a predefined, fixed length input. We adopt a sim-to-real methodology in which the network is first trained on simulated data and then fine-tuned on real trajectories. Experimental results demonstrate that the dense spatial sampling performed by event cameras significantly increases the number of intercepted trajectories compared to a fixed temporal sampling typical of traditional "frame-based"cameras. Results motivate further exploration of the use of event cameras for prediction in higher-complexity robotic tasks. © 2023 IEEE.

关键词： Cameras

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Unlocking Static Images for Training event-driven Neural Networks 8

Unlocking Static Images for Training Event-driven Neural Net...

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8th International Conference on event-Based Control, Communication, and Signal Processing, EBCCSP 2022

作者： Carissimi, Nicolo Goyal, Gaurvi Pietro, Franco Di Bartolozzi, Chiara Glover, Arren Italian Institute of Technology Event-driven Perception for Robotics Genova Italy

ISBN: (纸本)9781665453493

event driven cameras have the potential to revolutionise the real-time visual sensory processing paradigm. These asynchronous sensors detect change in the environment with low latency and high dynamic range, allowing for orders of magnitude faster systems than the state of the art using intensity cameras. On the other hand, deep artificial neural networks have refashioned machine vision in the last decade, greatly expanding the reach of viable tasks, supported by the creation of many large scale image datasets. In this work, we present a modality to leverage these large scale datasets for the purpose of training off-the-shelf deep learning architectures and re-appropriating them for event-based tasks. To this end, we describe an event representation, EROS, and a method to convert images to an EROS-like representation such that image datasets can train neural networks for event driven applications. © 2022 IEEE.

关键词： Cameras

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EDOPT: event-camera 6-DoF Dynamic Object Pose Tracking

EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking

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IEEE International Conference on robotics and Automation (ICRA)

作者： Arren Glover Luna Gava Zhichao Li Chiara Bartolozzi Event-Driven Perception for Robotics Istituto Italiano di Tecnologia Italy

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

High-frequency, low-latency, 6-DoF object tracking is useful for grasping objects in motion, taking robots beyond pick-and-place tasks. We propose using an event-camera for tracking the objects to leverage the low-latency and continuous (i.e. not fixed-rate) data capture for high-frequency tracking. We propose the EDOPT algorithm, which maintains real-time operation with a variable event-rate (which occurs due to variation in camera velocity and scene texture) and avoids frame-jumps and motion-blur which are problematic in traditional computer vision solutions. EDOPT uses a strong object prior, leading to a novel solution possible only with the event-camera. To our knowledge, this is the first method for 6-DoF object pose estimation with only the event-camera. The proposed method achieves comparable results to a state-of-the-art DNN technique that fuses frames, depth, and events. We demonstrate smooth, online object pose tracking with a live camera feed at > 300 Hz.

关键词： Training Heuristic algorithms Robot vision systems Pose estimation Cameras 6-DOF Real-time systems

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MoveEnet: Online High-Frequency Human Pose Estimation with an event Camera

MoveEnet: Online High-Frequency Human Pose Estimation with a...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Gaurvi Goyal Franco Di Pietro Nicolo Carissimi Arren Glover Chiara Bartolozzi Event-Driven Perception for Robotics Istituto Italiano di Tecnologia Italy

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Fast Trajectory End-Point Prediction with event Cameras for Reactive Robot Control

Fast Trajectory End-Point Prediction with Event Cameras for ...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Marco Monforte Luna Gava Massimiliano Iacono Arren Glover Chiara Bartolozzi Event-Driven Perception for Robotics Istituto Italiano di Tecnologia Italy

Prediction can be crucial for tasks with tight time constraints if a robot has limited speed and power. A low-latency, high-frequency perception system can reduce the time needed to converge on the expectation of the future state of the world, giving the robot additional time to act - or to choose a safer action. In this paper, we exploit event cameras for asynchronous motion-driven sampling, inherent data compression, and sub-millisecond latency to reduce the convergence time of a data-driven trajectory prediction algorithm. As a use-case, we use a Panda robotic arm to intercept a ball bouncing on a table. To predict the interception point as early as possible, and cope with the intrinsic variability of trajectory length - that cannot be defined a-priori for event cameras - we adopt a Stateful Long Short-Term Memory network, that asynchronously updates the prediction for each incoming point of the trajectory and does not require a predefined, fixed length input. We adopt a sim-to-real methodology in which the network is first trained on simulated data and then fine-tuned on real trajectories. Experimental results demonstrate that the dense spatial sampling performed by event cameras significantly increases the number of intercepted trajectories compared to a fixed temporal sampling typical of traditional "frame-based" cameras. Results motivate further exploration of the use of event cameras for prediction in higher-complexity robotic tasks.

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Fast Trajectory End-Point Prediction with event Cameras for Reactive Robot Control

arXiv

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arXiv 2023年

作者： Monforte, Marco Gava, Luna Iacono, Massimiliano Glover, Arren Bartolozzi, Chiara The Event-Driven Perception for Robotics Research Line Istituto Italiano di Tecnologia Italy

Prediction skills can be crucial for the success of tasks where robots have limited time to act or joints actuation power. In such a scenario, a vision system with a fixed, possibly too low, sampling rate could lead to the loss of informative points, slowing down prediction convergence and reducing the accuracy. In this paper, we propose to exploit the low latency, motion-driven sampling, and data compression properties of event cameras to overcome these issues. As a use-case, we use a Panda robotic arm to intercept a ball bouncing on a table. To predict the interception point, we adopt a Stateful LSTM network, a specific LSTM variant without fixed input length, which perfectly suits the event-driven paradigm and the problem at hand, where the length of the trajectory is not defined. We train the network in simulation to speed up the dataset acquisition and then fine-tune the models on real trajectories. Experimental results demonstrate how using a dense spatial sampling (i.e. event cameras) significantly increases the number of intercepted trajectories as compared to a fixed temporal sampling (i.e. frame-based cameras). Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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[WIP] Unlocking Static Images for Training event-driven Neural Networks

[WIP] Unlocking Static Images for Training Event-driven Neur...

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International Conference on event-based Control, Communication, and Signal Processing (EBCCSP)

作者： Nicolò Carissimi Gaurvi Goyal Franco Di Pietro Chiara Bartolozzi Arren Glover Event-driven Perception for Robotics Italian Institute of Technology Genova Italy

ISBN: (数字)9781665453493

ISBN: (纸本)9781665453509

关键词： Training Visualization Statistical analysis Machine vision Signal processing Cameras Sensor systems

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Hybrid Object Tracking with events and Frames

Hybrid Object Tracking with Events and Frames

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zhichao Li Nicola A. Piga Franco Di Pietro Massimiliano Iacono Arren Glover Lorenzo Natale Chiara Bartolozzi Humanoid Sensing and Perception Istituto Italiano di Tecnologia Italy Event-driven Perception for Robotics Istituto Italiano di Tecnologia Italy University of Genoa Genoa Italy

Robust object pose tracking plays an important role in robot manipulation, but it is still an open issue for quickly moving targets as motion blur and low frequency detection can reduce pose estimation accuracy even for state-of-the-art RGB-D-based methods. An event-camera is a low-latency vision sensor that can act complementary to RGB-D. Specifically, its sub-millisecond temporal resolution can be exploited to correct for pose estimation inaccuracies due to low frequency RGB-D based detection. To do so, we propose a dual Kalman filter: the first filter estimates an object's velocity from the spatiotemporal patterns of “events”, the second filter fuses the tracked object velocity with a low-frequency object pose estimated from a deep neural network using RGB-D data. The full system outputs high frequency, accurate object poses also for fast moving objects. The proposed method works towards low-power robotics by replacing high-cost GPU-based optical flow used in prior work with event-cameras that inherently extract the required signal without costly processing. The proposed algorithm achieves comparable or better performance when compared to two state-of-the-art 6-DoF object pose estimation algorithms and one hybrid event/RGB-D algorithm on benchmarks with simulated and real data. We discuss the benefits and tradeoffs for using the event-camera and contribute algorithm, code, and datasets to the community. The code and datasets are available at https://***/event-driven-robotics/Hybrid-object-tracking-with-events-and-frames.

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LuvHarris: A practical corner detector for event-cameras

arXiv

引用

arXiv 2021年

作者： Glover, Arren Dinale, Aiko Rosa, Leandro De Souza Bamford, Simeon Bartolozzi, Chiara Event-Driven Perception for Robotics group Istituto Italiano di Tecnologia Italy

There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput. Our method has two major contributions, 1. a novel "threshold ordinal event-surface" that removes certain tuning parameters and is well suited for Harris operations, and 2. an implementation of the Harris algorithm such that the computational load per event is minimised and computational heavy convolutions are performed only "as-fast-as-possible", i.e. only as computational resources are available. The result is a practical, real-time, and robust corner detector that runs more than 2.6x the speed of current state-of-the-art;a necessity when using high-resolution event-camera in real-time. We explain the considerations taken for the approach, compare the algorithm to current state-of-the-art in terms of computational performance and detection accuracy, and discuss the validity of the proposed approach for event cameras. © 2021, CC BY.

关键词： Cameras

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