Dynamic obstacle avoidance for quadrotors with event cameras

By Davide Falanga, Kevin Kleber, Davide Scaramuzza. Science Robotics, 2020

362 citations recorded by the source corpus.

Micro-aerial vehicles dodge fast-moving objects using only onboard sensing and computation. Today’s autonomous drones have reaction times of tens of milliseconds, which is not enough for navigating fast in complex dynamic environments. To safely avoid fast moving objects, drones need low-latency sensors and algorithms. We departed from state-of-the-art approaches by using event cameras, which are bioinspired sensors with reaction times of microseconds. Our approach exploits the temporal information contained in the event stream to distinguish between static and dynamic objects and leverages a fast strategy to generate the motor commands necessary to avoid the approaching obstacles. Standard vision algorithms cannot be applied to event cameras because the output of these sensors is not images but a stream of asynchronous events that encode per-pixel intensity changes. Our resulting algorithm has an overall latency of only 3.5 milliseconds, which is sufficient for reliable detection and avoidance of fast-moving obstacles. We demonstrate the effectiveness of our approach on an autonomous quadrotor using only onboard sensing and computation. Our drone was capable of avoiding multiple obstacles of different sizes and shapes, at relative speeds up to 10 meters/second, both indoors and outdoors.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/31d21edc3ed187c7f7bfa0aa16d185a9d45399c0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Electrical and Electronic Engineering

In Ocean Engineering

Citations in the corpus

Learning high-speed flight in the wild (2021, background): “Perception latency can possibly be reduced with event cameras [38], especially in the presence of dynamic obstacles [39].”
Low-latency automotive vision with event cameras (2024, neutral): “Frame-based sensors such as RGB cameras face a bandwidth–latency trade-off: higher frame rates reduce perceptual latency but increase bandwidth demands, whereas lower frame rates save bandwidth at the cost of missing vital scene dynamics due to increased perceptual latency 3 (Fig.”
Electronic skins and machine learning for intelligent soft robots (2020, background): “The bottom row contains examples (69, 128, 129) of these capabilities in current rigid robots, which we expect to further improve in parallel with the integration of e-skins, soft robotics, and machine learning.”