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of 64
pro vyhledávání: '"Gehrig, Daniel"'
Autor:
Jayanth, Royina Karegoudra, Xu, Yinshuang, Wang, Ziyun, Chatzipantazis, Evangelos, Gehrig, Daniel, Daniilidis, Kostas
Neural networks are seeing rapid adoption in purely inertial odometry, where accelerometer and gyroscope measurements from commodity inertial measurement units (IMU) are used to regress displacements and associated uncertainties. They can learn infor
Externí odkaz:
http://arxiv.org/abs/2408.06321
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Event cameras respond primarily to edges--formed by strong gradients--and are thus particularly well-suited for line-based motion estimation. Recent work has shown that events generated by a single line each satisfy a polynomial constraint which desc
Externí odkaz:
http://arxiv.org/abs/2404.00842
Publikováno v:
IEEE/CVF International Conference on Computer Vision (ICCV), 2023
Event-based cameras are ideal for line-based motion estimation, since they predominantly respond to edges in the scene. However, accurately determining the camera displacement based on events continues to be an open problem. This is because line feat
Externí odkaz:
http://arxiv.org/abs/2309.17054
Autor:
Pellerito, Roberto, Cannici, Marco, Gehrig, Daniel, Belhadj, Joris, Dubois-Matra, Olivier, Casasco, Massimo, Scaramuzza, Davide
Visual Odometry (VO) is crucial for autonomous robotic navigation, especially in GPS-denied environments like planetary terrains. To improve robustness, recent model-based VO systems have begun combining standard and event-based cameras. While event
Externí odkaz:
http://arxiv.org/abs/2309.09947
Autor:
Salah, Mohammed, Ayyad, Abdulla, Humais, Muhammad, Gehrig, Daniel, Abusafieh, Abdelqader, Seneviratne, Lakmal, Scaramuzza, Davide, Zweiri, Yahya
Event cameras triggered a paradigm shift in the computer vision community delineated by their asynchronous nature, low latency, and high dynamic range. Calibration of event cameras is always essential to account for the sensor intrinsic parameters an
Externí odkaz:
http://arxiv.org/abs/2306.09078
Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, selecting the appropriate representation for the task traditionally requir
Externí odkaz:
http://arxiv.org/abs/2304.13455
Publikováno v:
IEEE International Conference on Robotics and Automation (ICRA) 2023, London
Quadrupedal robots are conquering various indoor and outdoor applications due to their ability to navigate challenging uneven terrains. Exteroceptive information greatly enhances this capability since perceiving their surroundings allows them to adap
Externí odkaz:
http://arxiv.org/abs/2303.17479
Publikováno v:
IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, 2024
Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices through asynchronous and sparse processing. However, being temporal models, SNNs depend heavily on exp
Externí odkaz:
http://arxiv.org/abs/2303.14176
Autor:
Gehrig, Daniel, Scaramuzza, Davide
State-of-the-art machine-learning methods for event cameras treat events as dense representations and process them with conventional deep neural networks. Thus, they fail to maintain the sparsity and asynchronous nature of event data, thereby imposin
Externí odkaz:
http://arxiv.org/abs/2211.12324
Autor:
Mahlknecht, Florian, Gehrig, Daniel, Nash, Jeremy, Rockenbauer, Friedrich M., Morrell, Benjamin, Delaune, Jeff, Scaramuzza, Davide
Publikováno v:
IEEE Robotics and Automation Letters (RA-L), 2022
Due to their resilience to motion blur and high robustness in low-light and high dynamic range conditions, event cameras are poised to become enabling sensors for vision-based exploration on future Mars helicopter missions. However, existing event-ba
Externí odkaz:
http://arxiv.org/abs/2204.05880