38 research outputs found
Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation
The purpose of navigation is to determine the position, velocity, and
orientation of manned and autonomous platforms, humans, and animals. Obtaining
accurate navigation commonly requires fusion between several sensors, such as
inertial sensors and global navigation satellite systems, in a model-based,
nonlinear estimation framework. Recently, data-driven approaches applied in
various fields show state-of-the-art performance, compared to model-based
methods. In this paper we review multidisciplinary, data-driven based
navigation algorithms developed and experimentally proven at the Autonomous
Navigation and Sensor Fusion Lab (ANSFL) including algorithms suitable for
human and animal applications, varied autonomous platforms, and multi-purpose
navigation and fusion approachesComment: 22 pages, 13 figure
A Hybrid Adaptive Velocity Aided Navigation Filter with Application to INS/DVL Fusion
Autonomous underwater vehicles (AUV) are commonly used in many underwater
applications. Usually, inertial sensors and Doppler velocity log readings are
used in a nonlinear filter to estimate the AUV navigation solution. The process
noise covariance matrix is tuned according to the inertial sensors'
characteristics. This matrix greatly influences filter accuracy, robustness,
and performance. A common practice is to assume that this matrix is fixed
during the AUV operation. However, it varies over time as the amount of
uncertainty is unknown. Therefore, adaptive tuning of this matrix can lead to a
significant improvement in the filter performance. In this work, we propose a
learning-based adaptive velocity-aided navigation filter. To that end,
handcrafted features are generated and used to tune the momentary system noise
covariance matrix. Once the process noise covariance is learned, it is fed into
the model-based navigation filter. Simulation results show the benefits of our
approach compared to other adaptive approaches.Comment: 5 pages. arXiv admin note: substantial text overlap with
arXiv:2207.1208
Data-Driven Denoising of Stationary Accelerometer Signals
Modern navigation solutions are largely dependent on the performances of the
standalone inertial sensors, especially at times when no external sources are
available. During these outages, the inertial navigation solution is likely to
degrade over time due to instrumental noises sources, particularly when using
consumer low-cost inertial sensors. Conventionally, model-based estimation
algorithms are employed to reduce noise levels and enhance meaningful
information, thus improving the navigation solution directly. However,
guaranteeing their optimality often proves to be challenging as sensors
performance differ in manufacturing quality, process noise modeling, and
calibration precision. In the literature, most inertial denoising models are
model-based when recently several data-driven approaches were suggested
primarily for gyroscope measurements denoising. Data-driven approaches for
accelerometer denoising task are more challenging due to the unknown gravity
projection on the accelerometer axes. To fill this gap, we propose several
learning-based approaches and compare their performances with prominent
denoising algorithms, in terms of pure noise removal, followed by stationary
coarse alignment procedure. Based on the benchmarking results, obtained in
field experiments, we show that: (i) learning-based models perform better than
traditional signal processing filtering; (ii) non-parametric kNN algorithm
outperforms all state of the art deep learning models examined in this study;
(iii) denoising can be fruitful for pure inertial signal reconstruction, but
moreover for navigation-related tasks, as both errors are shown to be reduced
up to one order of magnitude.Comment: 10 pages, 15 figures, 8 table
Towards Learning-Based Gyrocompassing
Inertial navigation systems (INS) are widely used in both manned and
autonomous platforms. One of the most critical tasks prior to their operation
is to accurately determine their initial alignment while stationary, as it
forms the cornerstone for the entire INS operational trajectory. While
low-performance accelerometers can easily determine roll and pitch angles
(leveling), establishing the heading angle (gyrocompassing) with
low-performance gyros proves to be a challenging task without additional
sensors. This arises from the limited signal strength of Earth's rotation rate,
often overridden by gyro noise itself. To circumvent this deficiency, in this
study we present a practical deep learning framework to effectively compensate
for the inherent errors in low-performance gyroscopes. The resulting capability
enables gyrocompassing, thereby eliminating the need for subsequent prolonged
filtering phase (fine alignment). Through the development of theory and
experimental validation, we demonstrate that the improved initial conditions
establish a new lower error bound, bringing affordable gyros one step closer to
being utilized in high-end tactical tasks
Information Aided Navigation: A Review
The performance of inertial navigation systems is largely dependent on the
stable flow of external measurements and information to guarantee continuous
filter updates and bind the inertial solution drift. Platforms in different
operational environments may be prevented at some point from receiving external
measurements, thus exposing their navigation solution to drift. Over the years,
a wide variety of works have been proposed to overcome this shortcoming, by
exploiting knowledge of the system current conditions and turning it into an
applicable source of information to update the navigation filter. This paper
aims to provide an extensive survey of information aided navigation, broadly
classified into direct, indirect, and model aiding. Each approach is described
by the notable works that implemented its concept, use cases, relevant state
updates, and their corresponding measurement models. By matching the
appropriate constraint to a given scenario, one will be able to improve the
navigation solution accuracy, compensate for the lost information, and uncover
certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table
A-KIT: Adaptive Kalman-Informed Transformer
The extended Kalman filter (EKF) is a widely adopted method for sensor fusion
in navigation applications. A crucial aspect of the EKF is the online
determination of the process noise covariance matrix reflecting the model
uncertainty. While common EKF implementation assumes a constant process noise,
in real-world scenarios, the process noise varies, leading to inaccuracies in
the estimated state and potentially causing the filter to diverge. To cope with
such situations, model-based adaptive EKF methods were proposed and
demonstrated performance improvements, highlighting the need for a robust
adaptive approach. In this paper, we derive and introduce A-KIT, an adaptive
Kalman-informed transformer to learn the varying process noise covariance
online. The A-KIT framework is applicable to any type of sensor fusion. Here,
we present our approach to nonlinear sensor fusion based on an inertial
navigation system and Doppler velocity log. By employing real recorded data
from an autonomous underwater vehicle, we show that A-KIT outperforms the
conventional EKF by more than 49.5% and model-based adaptive EKF by an average
of 35.4% in terms of position accuracy
Learning Vehicle Trajectory Uncertainty
A novel approach for vehicle tracking using a hybrid adaptive Kalman filter
is proposed. The filter utilizes recurrent neural networks to learn the
vehicle's geometrical and kinematic features, which are then used in a
supervised learning model to determine the actual process noise covariance in
the Kalman framework. This approach addresses the limitations of traditional
linear Kalman filters, which can suffer from degraded performance due to
uncertainty in the vehicle kinematic trajectory modeling. Our method is
evaluated and compared to other adaptive filters using the Oxford RobotCar
dataset, and has shown to be effective in accurately determining the process
noise covariance in real-time scenarios. Overall, this approach can be
implemented in other estimation problems to improve performance
Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions
Inertial sensing is used in many applications and platforms, ranging from
day-to-day devices such as smartphones to very complex ones such as autonomous
vehicles. In recent years, the development of machine learning and deep
learning techniques has increased significantly in the field of inertial
sensing and sensor fusion. This is due to the development of efficient
computing hardware and the accessibility of publicly available sensor data.
These data-driven approaches mainly aim to empower model-based inertial sensing
algorithms. To encourage further research in integrating deep learning with
inertial navigation and fusion and to leverage their capabilities, this paper
provides an in-depth review of deep learning methods for inertial sensing and
sensor fusion. We discuss learning methods for calibration and denoising as
well as approaches for improving pure inertial navigation and sensor fusion.
The latter is done by learning some of the fusion filter parameters. The
reviewed approaches are classified by the environment in which the vehicles
operate: land, air, and sea. In addition, we analyze trends and future
directions in deep learning-based navigation and provide statistical data on
commonly used approaches
VIO-DualProNet: Visual-Inertial Odometry with Learning Based Process Noise Covariance
Visual-inertial odometry (VIO) is a vital technique used in robotics,
augmented reality, and autonomous vehicles. It combines visual and inertial
measurements to accurately estimate position and orientation. Existing VIO
methods assume a fixed noise covariance for the inertial uncertainty. However,
accurately determining in real-time the noise variance of the inertial sensors
presents a significant challenge as the uncertainty changes throughout the
operation leading to suboptimal performance and reduced accuracy. To circumvent
this, we propose VIO-DualProNet, a novel approach that utilizes deep learning
methods to dynamically estimate the inertial noise uncertainty in real-time. By
designing and training a deep neural network to predict inertial noise
uncertainty using only inertial sensor measurements, and integrating it into
the VINS-Mono algorithm, we demonstrate a substantial improvement in accuracy
and robustness, enhancing VIO performance and potentially benefiting other
VIO-based systems for precise localization and mapping across diverse
conditions.Comment: 10 pages, 15 figures, bib fil