Search Results for author: Guido C. H. E. de Croon

Found 17 papers, 8 papers with code

Lightweight Event-based Optical Flow Estimation via Iterative Deblurring

no code implementations24 Nov 2022 YiLun Wu, Federico Paredes-Vallés, Guido C. H. E. de Croon

Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit computation of correlation volumes, which are expensive to compute and store on systems with limited processing budget and memory.

Deblurring Optical Flow Estimation

NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter

no code implementations14 Sep 2022 Rik J. Bouwmeester, Federico Paredes-Vallés, Guido C. H. E. de Croon

In this work, we present NanoFlowNet, a lightweight convolutional neural network for real-time dense optical flow estimation on edge computing hardware.

Autonomous Navigation Edge-computing +2

CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry

1 code implementation30 Aug 2022 Yingfu Xu, Guido C. H. E. de Croon

Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world.

Motion Estimation Navigate

Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning

1 code implementation28 Mar 2022 Cheng Liu, Erik-Jan van Kampen, Guido C. H. E. de Croon

Enabling the capability of assessing risk and making risk-aware decisions is essential to applying reinforcement learning to safety-critical robots like drones.

Distributional Reinforcement Learning Drone navigation +3

EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following

no code implementations29 Jun 2021 Nitin J. Sanket, Chahat Deep Singh, Chethan M. Parameshwara, Cornelia Fermüller, Guido C. H. E. de Croon, Yiannis Aloimonos

Our network can detect propellers at a rate of 85. 1% even when 60% of the propeller is occluded and can run at upto 35Hz on a 2W power budget.

A model-based framework for learning transparent swarm behaviors

1 code implementation9 Mar 2021 Mario Coppola, Jian Guo, Eberhard Gill, Guido C. H. E. de Croon

The framework is based on the automatic extraction of two distinct models: 1) a neural network model trained to estimate the relationship between the robots' sensor readings and the global performance of the swarm, and 2) a probabilistic state transition model that explicitly models the local state transitions (i. e., transitions in observations from the perspective of a single robot in the swarm) given a policy.

MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models

1 code implementation5 Mar 2021 Daniël Willemsen, Mario Coppola, Guido C. H. E. de Croon

MAMBPO uses a learned world model to improve sample efficiency compared to model-free Multi-Agent Soft Actor-Critic (MASAC).

reinforcement-learning reinforcement Learning

CNN-based Ego-Motion Estimation for Fast MAV Maneuvers

2 code implementations6 Jan 2021 Yingfu Xu, Guido C. H. E. de Croon

In the field of visual ego-motion estimation for Micro Air Vehicles (MAVs), fast maneuvers stay challenging mainly because of the big visual disparity and motion blur.

Motion Estimation Self-Supervised Learning

An autonomous swarm of micro flying robots with range-based relative localization

1 code implementation12 Mar 2020 Shushuai Li, Mario Coppola, Christophe De Wagter, Guido C. H. E. de Croon

Accurate relative localization is an important requirement for a swarm of robots, especially when performing a cooperative task.

Robotics Multiagent Systems

Evolution of Robust High Speed Optical-Flow-Based Landing for Autonomous MAVs

no code implementations16 Dec 2019 Kirk Y. W. Scheper, Guido C. H. E. de Croon

Automatic optimization of robotic behavior has been the long-standing goal of Evolutionary Robotics.

Optical Flow Estimation

Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller

1 code implementation25 Sep 2019 Bardienus P. Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz, Colby R. Banbury, William Fu, Aleksandra Faust, Guido C. H. E. de Croon, Vijay Janapa Reddi

We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter.

Autonomous Navigation Efficient Exploration +1

How do neural networks see depth in single images?

no code implementations ICCV 2019 Tom van Dijk, Guido C. H. E. de Croon

We further show that MonoDepth's use of the vertical image position allows it to estimate the distance towards arbitrary obstacles, even those not appearing in the training set, but that it requires a strong edge at the ground contact point of the object to do so.

Monocular Depth Estimation

Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception

1 code implementation28 Jul 2018 Federico Paredes-Vallés, Kirk Y. W. Scheper, Guido C. H. E. de Croon

Convolutional layers with input synapses characterized by single and multiple transmission delays are employed for feature and local motion perception, respectively; while global motion selectivity emerges in a final fully-connected layer.

Event-based vision Optical Flow Estimation

Provable Emergent Pattern Formation by a Swarm of Anonymous, Homogeneous, Non-Communicating, Reactive Robots with Limited Relative Sensing and no Global Knowledge or Positioning

no code implementations18 Apr 2018 Mario Coppola, Jian Guo, Eberhard K. A. Gill, Guido C. H. E. de Croon

We then formally show that these local states can only coexist when the global desired pattern is achieved and that, until this occurs, there is always a sequence of actions that will lead from the current pattern to the desired pattern.

Robotics

Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow

no code implementations31 Jan 2017 Bas J. Pijnacker Hordijk, Kirk Y. W. Scheper, Guido C. H. E. de Croon

In addition, a method for estimating the divergence from event-based optical flow is introduced, which accounts for the aperture problem.

Optical Flow Estimation

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