13,379 research outputs found
3D Registration of Aerial and Ground Robots for Disaster Response: An Evaluation of Features, Descriptors, and Transformation Estimation
Global registration of heterogeneous ground and aerial mapping data is a
challenging task. This is especially difficult in disaster response scenarios
when we have no prior information on the environment and cannot assume the
regular order of man-made environments or meaningful semantic cues. In this
work we extensively evaluate different approaches to globally register UGV
generated 3D point-cloud data from LiDAR sensors with UAV generated point-cloud
maps from vision sensors. The approaches are realizations of different
selections for: a) local features: key-points or segments; b) descriptors:
FPFH, SHOT, or ESF; and c) transformation estimations: RANSAC or FGR.
Additionally, we compare the results against standard approaches like applying
ICP after a good prior transformation has been given. The evaluation criteria
include the distance which a UGV needs to travel to successfully localize, the
registration error, and the computational cost. In this context, we report our
findings on effectively performing the task on two new Search and Rescue
datasets. Our results have the potential to help the community take informed
decisions when registering point-cloud maps from ground robots to those from
aerial robots.Comment: Awarded Best Paper at the 15th IEEE International Symposium on
Safety, Security, and Rescue Robotics 2017 (SSRR 2017
Geometry Optimization of Crystals by the Quasi-Independent Curvilinear Coordinate Approximation
The quasi-independent curvilinear coordinate approximation (QUICCA) method
[K. N\'emeth and M. Challacombe, J. Chem. Phys. {\bf 121}, 2877, (2004)] is
extended to the optimization of crystal structures. We demonstrate that QUICCA
is valid under periodic boundary conditions, enabling simultaneous relaxation
of the lattice and atomic coordinates, as illustrated by tight optimization of
polyethylene, hexagonal boron-nitride, a (10,0) carbon-nanotube, hexagonal ice,
quartz and sulfur at the -point RPBE/STO-3G level of theory.Comment: Submitted to Journal of Chemical Physics on 7/7/0
TERRAIN-BASED NAVIGATION: A TOOL TO IMPROVE NAVIGATION AND FEATURE EXTRACTION PERFORMANCE OF MOBILE MAPPING SYSTEMS
Terrain-referenced navigation (TRN) techniques are of increasing interest in the research community, as they can provide alternative navigation tools when GPS is not available or the GPS signals are jammed. Some form of augmentation to cope with the lack of GPS signals is typically required in mobile mapping applications in urban canyons and is of interest for military applications. TRN could provide alternative position and attitude fixes to support an inertial navigation system, since such systems inevitably drift over time if not calibrated by GPS or other methodologies. With improving imaging sensor performance as well as growing worldwide availability of terrain high-resolution data and city models, terrain-based navigation is becoming a viable option to support navigation in GPS-denied environments. Furthermore, the feedback from the imaging sensors can be used even during GPS availability, which increases the redundancy of the measurement update step of the navigation filter, enabling more reliable integrity monitoring at this stage. The relevance of TRN to mobile mapping applications is twofold: (1) the process of obtaining real-time position and attitude fixes for the navigation filter is based on feature extraction, and, in particular, on the capability to separate the static and dynamic objects from the image data, and (2) the use of already available terrain data, including surface models (DSM), raster or vector data in CAD/GIS environments, such as city models, can effectively support the extraction processes. These two tasks could overlap, although the separation of the static and dynamic objects should work without any terrain data, and in fact, this is, to a large extent, the idea behind the removal of vehicles (moving objects) from imagery. The overall TRN concept, where LiDAR and optical imagery are matched with the existing terrain data is discussed and initial performance results are reported
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
Regularization Based Iterative Point Match Weighting for Accurate Rigid Transformation Estimation
Feature extraction and matching (FEM) for 3D shapes finds numerous applications in computer graphics and vision for object modeling, retrieval, morphing, and recognition. However, unavoidable incorrect matches lead to inaccurate estimation of the transformation relating different datasets. Inspired by AdaBoost, this paper proposes a novel iterative re-weighting method to tackle the challenging problem of evaluating point matches established by typical FEM methods. Weights are used to indicate the degree of belief that each point match is correct. Our method has three key steps: (i) estimation of the underlying transformation using weighted least squares, (ii) penalty parameter estimation via minimization of the weighted variance of the matching errors, and (iii) weight re-estimation taking into account both matching errors and information learnt in previous iterations. A comparative study, based on real shapes captured by two laser scanners, shows that the proposed method outperforms four other state-of-the-art methods in terms of evaluating point matches between overlapping shapes established by two typical FEM methods, resulting in more accurate estimates of the underlying transformation. This improved transformation can be used to better initialize the iterative closest point algorithm and its variants, making 3D shape registration more likely to succeed
X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments
Modern robotic systems are required to operate in challenging environments,
which demand reliable localization under challenging conditions. LiDAR-based
localization methods, such as the Iterative Closest Point (ICP) algorithm, can
suffer in geometrically uninformative environments that are known to
deteriorate point cloud registration performance and push optimization toward
divergence along weakly constrained directions. To overcome this issue, this
work proposes i) a robust fine-grained localizability detection module, and ii)
a localizability-aware constrained ICP optimization module, which couples with
the localizability detection module in a unified manner. The proposed
localizability detection is achieved by utilizing the correspondences between
the scan and the map to analyze the alignment strength against the principal
directions of the optimization as part of its fine-grained LiDAR localizability
analysis. In the second part, this localizability analysis is then integrated
into the scan-to-map point cloud registration to generate drift-free pose
updates by enforcing controlled updates or leaving the degenerate directions of
the optimization unchanged. The proposed method is thoroughly evaluated and
compared to state-of-the-art methods in simulated and real-world experiments,
demonstrating the performance and reliability improvement in LiDAR-challenging
environments. In all experiments, the proposed framework demonstrates accurate
and generalizable localizability detection and robust pose estimation without
environment-specific parameter tuning.Comment: 20 Pages, 20 Figures Submitted to IEEE Transactions On Robotics.
Supplementary Video: https://youtu.be/SviLl7q69aA Project Website:
https://sites.google.com/leggedrobotics.com/x-ic
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