164 citations · 323 across the 19 of their papers we have counts for
6 papers · 1 filter
A 5-Point Minimal Solver for Event Camera Relative Motion Estimation
Ling Gao, Hang Su, Daniel Gehrig +3
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 base…
Deep Visual Odometry with Events and Frames
Roberto Pellerito, Marco Cannici, Daniel Gehrig +4
Visual Odometry (VO) is crucial for autonomous robotic navigation, especially in GPS-denied environments like planetary terrains. To improve robustness, recent model-based VO syste…
E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras
Mohammed Salah, Abdulla Ayyad, Muhammad Humais +5
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…
Event-based Agile Object Catching with a Quadrupedal Robot
Benedek Forrai, Takahiro Miki, Daniel Gehrig +2
Quadrupedal robots are conquering various indoor and outdoor applications due to their ability to navigate challenging uneven terrains. Exteroceptive information greatly enhances t…
From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection
Nikola Zubić, Daniel Gehrig, Mathias Gehrig +1
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, sele…
A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception
Asude Aydin, Mathias Gehrig, Daniel Gehrig +1
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…