6,174 research outputs found
Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data
In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing
Deep Forward and Inverse Perceptual Models for Tracking and Prediction
We consider the problems of learning forward models that map state to
high-dimensional images and inverse models that map high-dimensional images to
state in robotics. Specifically, we present a perceptual model for generating
video frames from state with deep networks, and provide a framework for its use
in tracking and prediction tasks. We show that our proposed model greatly
outperforms standard deconvolutional methods and GANs for image generation,
producing clear, photo-realistic images. We also develop a convolutional neural
network model for state estimation and compare the result to an Extended Kalman
Filter to estimate robot trajectories. We validate all models on a real robotic
system.Comment: 8 pages, International Conference on Robotics and Automation (ICRA)
201
Deep Drone Racing: From Simulation to Reality with Domain Randomization
Dynamically changing environments, unreliable state estimation, and operation
under severe resource constraints are fundamental challenges that limit the
deployment of small autonomous drones. We address these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. A
racing drone must traverse a track with possibly moving gates at high speed. We
enable this functionality by combining the performance of a state-of-the-art
planning and control system with the perceptual awareness of a convolutional
neural network (CNN). The resulting modular system is both platform- and
domain-independent: it is trained in simulation and deployed on a physical
quadrotor without any fine-tuning. The abundance of simulated data, generated
via domain randomization, makes our system robust to changes of illumination
and gate appearance. To the best of our knowledge, our approach is the first to
demonstrate zero-shot sim-to-real transfer on the task of agile drone flight.
We extensively test the precision and robustness of our system, both in
simulation and on a physical platform, and show significant improvements over
the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics
Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854
Online Visual Robot Tracking and Identification using Deep LSTM Networks
Collaborative robots working on a common task are necessary for many
applications. One of the challenges for achieving collaboration in a team of
robots is mutual tracking and identification. We present a novel pipeline for
online visionbased detection, tracking and identification of robots with a
known and identical appearance. Our method runs in realtime on the limited
hardware of the observer robot. Unlike previous works addressing robot tracking
and identification, we use a data-driven approach based on recurrent neural
networks to learn relations between sequential inputs and outputs. We formulate
the data association problem as multiple classification problems. A deep LSTM
network was trained on a simulated dataset and fine-tuned on small set of real
data. Experiments on two challenging datasets, one synthetic and one real,
which include long-term occlusions, show promising results.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Awar
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