6 accepted papers
We consider the task of visually estimating the relative pose of a drone racing gate in front of a nano-quadrotor, using a convolutional neural network pre-trained on simulated data to regress the gate's pose. Due to the sim-to-real gap, the pre-trained model underperforms in the real world and must…
Sub-30 g nano-sized aerial robots can leverage their agility and form factor to explore cluttered and narrow environments, like in industrial inspection and search and rescue missions. However, the price for their tiny size is a strong limit in their resources, i.e., sub-100 mW microcontroller units…
Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles' perception, planning, and control skills. The recent emergence of autonomous nano-sized drone racing imposes new challenges, as their <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http
Presents corrections to the article “a Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing”.
Sub-50g nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception despite severe hardware constraints (i.e., sub-100mW processor). When deployed in unknown environments not represented in the training data,…
Nano-quadcopters are versatile platforms attracting the interest of both academia and industry. Their tiny form factor, i.e., ~ 10 cm diameter, makes them particularly useful in narrow scenarios and harmless in human proximity. However, these advantages come at the price of ultra-constrained onboard…