14 accepted papers
Object pose tracking is a fundamental and essential task for robotics to perform tasks in the home and industrial settings. The most commonly used sensors to do so are RGB-D cameras, which can hit limitations in highly dynamic environments due to motion blur and frame-rate constraints. Event cameras…
Tracking the position and orientation of objects in space (i.e., in 6-DoF) in real time is a fundamental problem in robotics for environment interaction. It becomes more challenging when objects move at high-speed due to frame rate limitations in conventional cameras and motion blur. Event cameras a
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.…
Event cameras offer low-latency and data compression for visual applications, through event-driven operation, that can be exploited for edge processing in tiny autonomous agents. Robust, accurate and low latency extraction of highly informative features such as corners is key for most visual process…
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 se…
Fast, non-linear trajectories have been shown to be more accurately visually measured, and hence predicted, when sampled spatially (that is when the target position changes) rather than temporally, i.e. at a fixed-rate as in traditional frame-based cameras. Event-cameras, with their asynchronous, lo…
Autonomous robots can rely on attention mechanisms to explore complex scenes and select salient stimuli relevant for behaviour. Stimulus selection should be fast to efficiently allocate available (and limited) computational resources to process in detail a subset of the otherwise overwhelmingly larg…
Event cameras offer many advantages for dynamic robotics due to their low latency response to motion, high dynamic range, and inherent compression of the visual signal. Many algorithms easily achieve real-time performance when testing on off-line datasets, however with an increase in camera resoluti…
Event cameras are an emerging technology in computer vision, offering extremely low latency and bandwidth, as well as a high temporal resolution and dynamic range. Inherent data compression is achieved as pixel data is only produced by contrast changes at the edges of moving objects. However, curren…
We propose a method to compress the enormous amount of data originating from tactile sensors is presented that explicitly exploits the inherent sparseness over space and time, sending tactile “events” only when a contact is detected. The resulting modular architecture is based on FPGA modules that a…
Event cameras are a new technology that can enable low-latency, fast visual sensing in dynamic environments towards faster robotic vision as they respond only to changes in the scene and have a very high temporal resolution (<; 1μs). Moving targets produce dense spatio-temporal streams of events tha…
The fast temporal-dynamics and intrinsic motion segmentation of event-based cameras are beneficial for robotic tasks that require low-latency visual tracking and control, for example a robot catching a ball. When the event-driven iCub humanoid robot grasps an object its head and torso move, inducing…
The detection of consistent feature points in an image is fundamental for various kinds of computer vision techniques, such as stereo matching, object recognition, target tracking and optical flow computation. This paper presents an event-based approach to the detection of corner points, which benef…
Event-driven vision sensors have the potential to support a new generation of efficient and robust robots. This requires the development of a new computational framework that exploits not only the spatial information, like in the traditional frame-based approach, but also the temporal content of the…