776 research outputs found
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
Existing simultaneous localization and mapping (SLAM) algorithms are not
robust in challenging low-texture environments because there are only few
salient features. The resulting sparse or semi-dense map also conveys little
information for motion planning. Though some work utilize plane or scene layout
for dense map regularization, they require decent state estimation from other
sources. In this paper, we propose real-time monocular plane SLAM to
demonstrate that scene understanding could improve both state estimation and
dense mapping especially in low-texture environments. The plane measurements
come from a pop-up 3D plane model applied to each single image. We also combine
planes with point based SLAM to improve robustness. On a public TUM dataset,
our algorithm generates a dense semantic 3D model with pixel depth error of 6.2
cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our
method creates a much better 3D model with state estimation error of 0.67%.Comment: International Conference on Intelligent Robots and Systems (IROS)
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Adolescent mental health problems in early stages of the COVID-19 pandemic were masked by lockdown measures and restrictions.
In the BJPsych Open Wong et al examined the influence of lockdown stringency during early stages of the COVID-19 pandemic on psychiatric emergency presentations among children and adolescents from ten countries. Data from March and April 2019 were compared with the same time frame in 2020, with particular focus on self-harm admissions. In this editorial, the publication is summarised and potential implications for the field and future studies are discussed
Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation
We consider the problem of learning observation models for robot state
estimation with incremental non-differentiable optimizers in the loop.
Convergence to the correct belief over the robot state is heavily dependent on
a proper tuning of observation models which serve as input to the optimizer. We
propose a gradient-based learning method which converges much quicker to model
estimates that lead to solutions of much better quality compared to an existing
state-of-the-art method as measured by the tracking accuracy over unseen robot
test trajectories.Comment: 6 pages, 4 figures. Published at the Differentiable Almost Everything
Workshop of the 40th International Conference on Machine Learnin
SONIC: Sonar Image Correspondence using Pose Supervised Learning for Imaging Sonars
In this paper, we address the challenging problem of data association for
underwater SLAM through a novel method for sonar image correspondence using
learned features. We introduce SONIC (SONar Image Correspondence), a
pose-supervised network designed to yield robust feature correspondence capable
of withstanding viewpoint variations. The inherent complexity of the underwater
environment stems from the dynamic and frequently limited visibility
conditions, restricting vision to a few meters of often featureless expanses.
This makes camera-based systems suboptimal in most open water application
scenarios. Consequently, multibeam imaging sonars emerge as the preferred
choice for perception sensors. However, they too are not without their
limitations. While imaging sonars offer superior long-range visibility compared
to cameras, their measurements can appear different from varying viewpoints.
This inherent variability presents formidable challenges in data association,
particularly for feature-based methods. Our method demonstrates significantly
better performance in generating correspondences for sonar images which will
pave the way for more accurate loop closure constraints and sonar-based place
recognition. Code as well as simulated and real-world datasets will be made
public to facilitate further development in the field
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