242 research outputs found
Flexible Stereo: Constrained, Non-rigid, Wide-baseline Stereo Vision for Fixed-wing Aerial Platforms
This paper proposes a computationally efficient method to estimate the
time-varying relative pose between two visual-inertial sensor rigs mounted on
the flexible wings of a fixed-wing unmanned aerial vehicle (UAV). The estimated
relative poses are used to generate highly accurate depth maps in real-time and
can be employed for obstacle avoidance in low-altitude flights or landing
maneuvers. The approach is structured as follows: Initially, a wing model is
identified by fitting a probability density function to measured deviations
from the nominal relative baseline transformation. At run-time, the prior
knowledge about the wing model is fused in an Extended Kalman filter~(EKF)
together with relative pose measurements obtained from solving a relative
perspective N-point problem (PNP), and the linear accelerations and angular
velocities measured by the two inertial measurement units (IMU) which are
rigidly attached to the cameras. Results obtained from extensive synthetic
experiments demonstrate that our proposed framework is able to estimate highly
accurate baseline transformations and depth maps.Comment: Accepted for publication in IEEE International Conference on Robotics
and Automation (ICRA), 2018, Brisban
Request for the review of the GSP status of the Republic of the Philippines for violations of worker rights
The ILRF filed this request to review the Philippines designation as a beneficiary of the Generalized System of Preferences due to the Republic’s failure to afford workers “internationally recognized workers rights.
Extended Preintegration for Relative State Estimation of Leader-Follower Platform
Relative state estimation using exteroceptive sensors suffers from
limitations of the field of view (FOV) and false detection, that the
proprioceptive sensor (IMU) data are usually engaged to compensate. Recently
ego-motion constraint obtained by Inertial measurement unit (IMU)
preintegration has been extensively used in simultaneous localization and
mapping (SLAM) to alleviate the computation burden. This paper introduces an
extended preintegration incorporating the IMU preintegration of two platforms
to formulate the motion constraint of relative state. One merit of this
analytic constraint is that it can be seamlessly integrated into the unified
graph optimization framework to implement the relative state estimation in a
high-performance real-time tracking thread, another point is a full smoother
design with this precise constraint to optimize the 3D coordinate and refine
the state for the refinement thread. We compare extensively in simulations the
proposed algorithms with two existing approaches to confirm our outperformance.
In the real virtual reality (VR) application design with the proposed
estimator, we properly realize the visual tracking of the six degrees of
freedom (6DoF) controller suitable for almost all scenarios, including the
challenging environment with missing features, light mutation, dynamic scenes,
etc. The demo video is at https://www.youtube.com/watch?v=0idb9Ls2iAM. For the
benefit of the community, we make the source code public
CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network
Deep learning has been widely adopted for channel state information
(CSI)-fingerprinting indoor localization systems. These systems usually consist
of two main parts, i.e., a positioning network that learns the mapping from
high-dimensional CSI to physical locations and a tracking system that utilizes
historical CSI to reduce the positioning error. This paper presents a new
localization system with high accuracy and generality. On the one hand, the
receptive field of the existing convolutional neural network (CNN)-based
positioning networks is limited, restricting their performance as useful
information in CSI is not explored thoroughly. As a solution, we propose a
novel attention-augmented residual CNN to utilize the local information and
global context in CSI exhaustively. On the other hand, considering the
generality of a tracking system, we decouple the tracking system from the CSI
environments so that one tracking system for all environments becomes possible.
Specifically, we remodel the tracking problem as a denoising task and solve it
with deep trajectory prior. Furthermore, we investigate how the precision
difference of inertial measurement units will adversely affect the tracking
performance and adopt plug-and-play to solve the precision difference problem.
Experiments show the superiority of our methods over existing approaches in
performance and generality improvement.Comment: 32 pages, Added references in section 2,3; Added explanations for
some academic terms; Corrected typos; Added experiments in section 5,
previous results unchanged; is under review for possible publicatio
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