8 research outputs found
Range, Endurance, and Optimal Speed Estimates for Multicopters
Multicopters are among the most versatile mobile robots. Their applications range from inspection and mapping tasks to providing vital reconnaissance in disaster zones and to package delivery. The range, endurance, and speed a multirotor vehicle can achieve while performing its task is a decisive factor not only for vehicle design and mission planning, but also for policy makers deciding on the rules and regulations for aerial robots. To the best of the authors’ knowledge, this work proposes the first approach to estimate the range, endurance, and optimal flight speed for a wide variety of multicopters. This advance is made possible by combining a state-of-the-art first-principles aerodynamic multicopter model based on blade-element-momentum theory with an electric-motor model and a graybox battery model. This model predicts the cell voltage with only 1.3% relative error ( 43.1mV ), even if the battery is subjected to non-constant discharge rates. Our approach is validated with real-world experiments on a test bench as well as with flights at speeds up to 65km/h in one of the world’s largest motion-capture systems. We also present an accurate pen-and-paper algorithm to estimate the range, endurance and optimal speed of multicopters to help future researchers build drones with maximal range and endurance, ensuring that future multirotor vehicles are even more versatile
HDVIO: Improving Localization and Disturbance Estimation with Hybrid Dynamics VIO
Visual-inertial odometry (VIO) is the most common approach for estimating the
state of autonomous micro aerial vehicles using only onboard sensors. Existing
methods improve VIO performance by including a dynamics model in the estimation
pipeline. However, such methods degrade in the presence of low-fidelity vehicle
models and continuous external disturbances, such as wind. Our proposed method,
HDVIO, overcomes these limitations by using a hybrid dynamics model that
combines a point-mass vehicle model with a learning-based component that
captures complex aerodynamic effects. HDVIO estimates the external force and
the full robot state by leveraging the discrepancy between the actual motion
and the predicted motion of the hybrid dynamics model. Our hybrid dynamics
model uses a history of thrust and IMU measurements to predict the vehicle
dynamics. To demonstrate the performance of our method, we present results on
both public and novel drone dynamics datasets and show real-world experiments
of a quadrotor flying in strong winds up to 25 km/h. The results show that our
approach improves the motion and external force estimation compared to the
state-of-the-art by up to 33% and 40%, respectively. Furthermore, differently
from existing methods, we show that it is possible to predict the vehicle
dynamics accurately while having no explicit knowledge of its full state
Learned Inertial Odometry for Autonomous Drone Racing
Inertial odometry is an attractive solution to the problem of state
estimation for agile quadrotor flight. It is inexpensive, lightweight, and it
is not affected by perceptual degradation. However, only relying on the
integration of the inertial measurements for state estimation is infeasible.
The errors and time-varying biases present in such measurements cause the
accumulation of large drift in the pose estimates. Recently, inertial odometry
has made significant progress in estimating the motion of pedestrians.
State-of-the-art algorithms rely on learning a motion prior that is typical of
humans but cannot be transferred to drones. In this work, we propose a
learning-based odometry algorithm that uses an inertial measurement unit (IMU)
as the only sensor modality for autonomous drone racing tasks. The core idea of
our system is to couple a model-based filter, driven by the inertial
measurements, with a learning-based module that has access to the control
commands. We show that our inertial odometry algorithm is superior to the
state-of-the-art filter-based and optimization-based visual- inertial odometry
as well as the state-of-the-art learned-inertial odometry. Additionally, we
show that our system is comparable to a visual-inertial odometry solution that
uses a camera and exploits the known gate location and appearance. We believe
that the application in autonomous drone racing paves the way for novel
research in inertial odometry for agile quadrotor flight. We will release the
code upon acceptance
Event-based Shape from Polarization
State-of-the-art solutions for Shape-from-Polarization (SfP) suffer from a
speed-resolution tradeoff: they either sacrifice the number of polarization
angles measured or necessitate lengthy acquisition times due to framerate
constraints, thus compromising either accuracy or latency. We tackle this
tradeoff using event cameras. Event cameras operate at microseconds resolution
with negligible motion blur, and output a continuous stream of events that
precisely measures how light changes over time asynchronously. We propose a
setup that consists of a linear polarizer rotating at high-speeds in front of
an event camera. Our method uses the continuous event stream caused by the
rotation to reconstruct relative intensities at multiple polarizer angles.
Experiments demonstrate that our method outperforms physics-based baselines
using frames, reducing the MAE by 25% in synthetic and real-world dataset. In
the real world, we observe, however, that the challenging conditions (i.e.,
when few events are generated) harm the performance of physics-based solutions.
To overcome this, we propose a learning-based approach that learns to estimate
surface normals even at low event-rates, improving the physics-based approach
by 52% on the real world dataset. The proposed system achieves an acquisition
speed equivalent to 50 fps (>twice the framerate of the commercial polarization
sensor) while retaining the spatial resolution of 1MP. Our evaluation is based
on the first large-scale dataset for event-based SfPComment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
Vancouver, 202
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight
Scene transfer for vision-based mobile robotics applications is a highly
relevant and challenging problem. The utility of a robot greatly depends on its
ability to perform a task in the real world, outside of a well-controlled lab
environment. Existing scene transfer end-to-end policy learning approaches
often suffer from poor sample efficiency or limited generalization
capabilities, making them unsuitable for mobile robotics applications. This
work proposes an adaptive multi-pair contrastive learning strategy for visual
representation learning that enables zero-shot scene transfer and real-world
deployment. Control policies relying on the embedding are able to operate in
unseen environments without the need for finetuning in the deployment
environment. We demonstrate the performance of our approach on the task of
agile, vision-based quadrotor flight. Extensive simulation and real-world
experiments demonstrate that our approach successfully generalizes beyond the
training domain and outperforms all baselines
NeuroBEM: Hybrid Aerodynamic Quadrotor Model
Quadrotors are extremely agile, so much in fact, that classic
first-principle-models come to their limits. Aerodynamic effects, while
insignificant at low speeds, become the dominant model defect during high
speeds or agile maneuvers. Accurate modeling is needed to design robust
high-performance control systems and enable flying close to the platform's
physical limits. We propose a hybrid approach fusing first principles and
learning to model quadrotors and their aerodynamic effects with unprecedented
accuracy. First principles fail to capture such aerodynamic effects, rendering
traditional approaches inaccurate when used for simulation or controller
tuning. Data-driven approaches try to capture aerodynamic effects with blackbox
modeling, such as neural networks; however, they struggle to robustly
generalize to arbitrary flight conditions. Our hybrid approach unifies and
outperforms both first-principles blade-element theory and learned residual
dynamics. It is evaluated in one of the world's largest motion-capture systems,
using autonomous-quadrotor-flight data at speeds up to 65km/h. The resulting
model captures the aerodynamic thrust, torques, and parasitic effects with
astonishing accuracy, outperforming existing models with 50% reduced prediction
errors, and shows strong generalization capabilities beyond the training set.Comment: 9 pages + 1 pages reference
RTOB SLAM: Real-Time Onboard Laser-Based Localization and Mapping
RTOB-SLAM is a new low-computation framework for real-time onboard simultaneous localization and mapping (SLAM) and obstacle avoidance for autonomous vehicles. A low-resolution 2D laser scanner is used and a small form-factor computer perform all computations onboard. The SLAM process is based on laser scan matching with the iterative closest point technique to estimate the vehicle's current position by aligning the new scan with the map. This paper describes a new method which uses only a small subsample of the global map for scan matching, which improves the performance and allows for a map to adapt to a dynamic environment by partly forgetting the past. A detailed comparison between this method and current state-of-the-art SLAM frameworks is given, together with a methodology to choose the parameters of the RTOB-SLAM. The RTOB-SLAM has been implemented in ROS and perform well in various simulations and real experiments
Cracking double-blind review: Authorship attribution with deep learning.
Double-blind peer review is considered a pillar of academic research because it is perceived to ensure a fair, unbiased, and fact-centered scientific discussion. Yet, experienced researchers can often correctly guess from which research group an anonymous submission originates, biasing the peer-review process. In this work, we present a transformer-based, neural-network architecture that only uses the text content and the author names in the bibliography to attribute an anonymous manuscript to an author. To train and evaluate our method, we created the largest authorship-identification dataset to date. It leverages all research papers publicly available on arXiv amounting to over 2 million manuscripts. In arXiv-subsets with up to 2,000 different authors, our method achieves an unprecedented authorship attribution accuracy, where up to 73% of papers are attributed correctly. We present a scaling analysis to highlight the applicability of the proposed method to even larger datasets when sufficient compute capabilities are more widely available to the academic community. Furthermore, we analyze the attribution accuracy in settings where the goal is to identify all authors of an anonymous manuscript. Thanks to our method, we are not only able to predict the author of an anonymous work but we also provide empirical evidence of the key aspects that make a paper attributable. We have open-sourced the necessary tools to reproduce our experiments