1,933 research outputs found
Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
In this paper we introduce Co-Fusion, a dense SLAM system that takes a live
stream of RGB-D images as input and segments the scene into different objects
(using either motion or semantic cues) while simultaneously tracking and
reconstructing their 3D shape in real time. We use a multiple model fitting
approach where each object can move independently from the background and still
be effectively tracked and its shape fused over time using only the information
from pixels associated with that object label. Previous attempts to deal with
dynamic scenes have typically considered moving regions as outliers, and
consequently do not model their shape or track their motion over time. In
contrast, we enable the robot to maintain 3D models for each of the segmented
objects and to improve them over time through fusion. As a result, our system
can enable a robot to maintain a scene description at the object level which
has the potential to allow interactions with its working environment; even in
the case of dynamic scenes.Comment: International Conference on Robotics and Automation (ICRA) 2017,
http://visual.cs.ucl.ac.uk/pubs/cofusion,
https://github.com/martinruenz/co-fusio
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
Robust Dense Mapping for Large-Scale Dynamic Environments
We present a stereo-based dense mapping algorithm for large-scale dynamic
urban environments. In contrast to other existing methods, we simultaneously
reconstruct the static background, the moving objects, and the potentially
moving but currently stationary objects separately, which is desirable for
high-level mobile robotic tasks such as path planning in crowded environments.
We use both instance-aware semantic segmentation and sparse scene flow to
classify objects as either background, moving, or potentially moving, thereby
ensuring that the system is able to model objects with the potential to
transition from static to dynamic, such as parked cars. Given camera poses
estimated from visual odometry, both the background and the (potentially)
moving objects are reconstructed separately by fusing the depth maps computed
from the stereo input. In addition to visual odometry, sparse scene flow is
also used to estimate the 3D motions of the detected moving objects, in order
to reconstruct them accurately. A map pruning technique is further developed to
improve reconstruction accuracy and reduce memory consumption, leading to
increased scalability. We evaluate our system thoroughly on the well-known
KITTI dataset. Our system is capable of running on a PC at approximately 2.5Hz,
with the primary bottleneck being the instance-aware semantic segmentation,
which is a limitation we hope to address in future work. The source code is
available from the project website (http://andreibarsan.github.io/dynslam).Comment: Presented at IEEE International Conference on Robotics and Automation
(ICRA), 201
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
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