29,095 research outputs found
On fusion for robust motion segmentation
While a multitude of motion segmentation algorithms have been presented in the literature, there has not been an objective assessment of different approaches to fusing their outputs. This paper investigates the application of 4 different fusion schemes to the outputs of 3 probabilistic pixel-level segmentation algorithms. We performed an extensive experimentation using 6 challenge categories from the changedetection.net dataset demonstrating that in general simple majority vote proves to be more effective than more complex fusion schemes
Robust dense visual SLAM using sensor fusion and motion segmentation
Visual simultaneous localisation and mapping (SLAM) is an important technique for
enabling mobile robots to navigate autonomously within their environments. Using
cameras, robots reconstruct a representation of their environment and simultaneously
localise themselves within it. A dense visual SLAM system produces a high-resolution
and detailed reconstruction of the environment which can be used for obstacle avoidance or semantic reasoning.
State-of-the-art dense visual SLAM systems demonstrate robust performance and
impressive accuracy in ideal conditions. However, these techniques are based on requirements which limit the extent to which they can be deployed in real applications.
Fundamentally, they require constant scene illumination, smooth camera motion and
no moving objects being present in the scene. Overcoming these requirements is not
trivial and significant effort is needed to make dense visual SLAM approaches more
robust to real-world conditions.
The objective of this thesis is to develop dense visual SLAM systems which are
more robust to real-world visually challenging conditions. For this, we leverage sensor
fusion and motion segmentation for situations where camera data is unsuitable.
The first contribution is a visual SLAM system for the NASA Valkyrie humanoid
robot which is robust to the robot’s operation. It is based on a sensor fusion approach
which combines visual SLAM and leg odometry to demonstrate increased robustness
to illumination changes and fast camera motion.
Second, we research methods for robust visual odometry in the presence of moving
objects. We propose a formulation for joint visual odometry and motion segmentation
that demonstrates increased robustness in scenes with moving objects compared to
state-of-the-art approaches.
We then extend this method using inertial information from a gyroscope to compare the contributions of motion segmentation and motion prior integration for robustness to scene dynamics. As part of this study we provide a dataset recorded in
scenes with different numbers of moving objects.
In conclusion, we find that both motion segmentation and motion prior integration
are necessary for achieving significantly better results in real-world conditions. While
motion priors increase robustness, motion segmentation increases the accuracy of the
reconstruction results through filtering of moving objects.Edinburgh Centre for RoboticsEngineering and Physical Sciences Research Council (EPSRC
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
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
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