11,081 research outputs found
An Equivariant Observer Design for Visual Localisation and Mapping
This paper builds on recent work on Simultaneous Localisation and Mapping
(SLAM) in the non-linear observer community, by framing the visual localisation
and mapping problem as a continuous-time equivariant observer design problem on
the symmetry group of a kinematic system. The state-space is a quotient of the
robot pose expressed on SE(3) and multiple copies of real projective space,
used to represent both points in space and bearings in a single unified
framework. An observer with decoupled Riccati-gains for each landmark is
derived and we show that its error system is almost globally asymptotically
stable and exponentially stable in-the-large.Comment: 12 pages, 2 figures, published in 2019 IEEE CD
EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers
Ultrasound (US) is the most widely used fetal imaging technique. However, US
images have limited capture range, and suffer from view dependent artefacts
such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a
high-resolution volume can extend the field of view and remove image artefacts,
which is useful for retrospective analysis including population based studies.
However, such volume reconstructions require information about relative
transformations between probe positions from which the individual volumes were
acquired. In prenatal US scans, the fetus can move independently from the
mother, making external trackers such as electromagnetic or optical tracking
unable to track the motion between probe position and the moving fetus. We
provide a novel methodology for image-based tracking and volume reconstruction
by combining recent advances in deep learning and simultaneous localisation and
mapping (SLAM). Tracking semantics are established through the use of a
Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of
concept, experiments are conducted on US volumes taken from a whole body fetal
phantom, and from the heads of real fetuses. For the fetal head segmentation,
we also introduce a novel weak annotation approach to minimise the required
manual effort for ground truth annotation. We evaluate our method
qualitatively, and quantitatively with respect to tissue discrimination
accuracy and tracking robustness.Comment: MICCAI Workshop on Perinatal, Preterm and Paediatric Image analysis
(PIPPI), 201
LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles
A key capability for autonomous underground mining vehicles is real-time
accurate localisation. While significant progress has been made, currently
deployed systems have several limitations ranging from dependence on costly
additional infrastructure to failure of both visual and range sensor-based
techniques in highly aliased or visually challenging environments. In our
previous work, we presented a lightweight coarse vision-based localisation
system that could map and then localise to within a few metres in an
underground mining environment. However, this level of precision is
insufficient for providing a cheaper, more reliable vision-based automation
alternative to current range sensor-based systems. Here we present a new
precision localisation system dubbed "LookUP", which learns a
neural-network-based pixel sampling strategy for estimating homographies based
on ceiling-facing cameras without requiring any manual labelling. This new
system runs in real time on limited computation resource and is demonstrated on
two different underground mine sites, achieving real time performance at ~5
frames per second and a much improved average localisation error of ~1.2 metre.Comment: 7 pages, 7 figures, accepted for IEEE ICRA 201
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal
Model-free reinforcement learning has recently been shown to be effective at
learning navigation policies from complex image input. However, these
algorithms tend to require large amounts of interaction with the environment,
which can be prohibitively costly to obtain on robots in the real world. We
present an approach for efficiently learning goal-directed navigation policies
on a mobile robot, from only a single coverage traversal of recorded data. The
navigation agent learns an effective policy over a diverse action space in a
large heterogeneous environment consisting of more than 2km of travel, through
buildings and outdoor regions that collectively exhibit large variations in
visual appearance, self-similarity, and connectivity. We compare pretrained
visual encoders that enable precomputation of visual embeddings to achieve a
throughput of tens of thousands of transitions per second at training time on a
commodity desktop computer, allowing agents to learn from millions of
trajectories of experience in a matter of hours. We propose multiple forms of
computationally efficient stochastic augmentation to enable the learned policy
to generalise beyond these precomputed embeddings, and demonstrate successful
deployment of the learned policy on the real robot without fine tuning, despite
environmental appearance differences at test time. The dataset and code
required to reproduce these results and apply the technique to other datasets
and robots is made publicly available at rl-navigation.github.io/deployable
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