33,022 research outputs found
Improved Fourier Mellin Invariant for Robust Rotation Estimation with Omni-cameras
Spectral methods such as the improved Fourier Mellin Invariant (iFMI)
transform have proved faster, more robust and accurate than feature based
methods on image registration. However, iFMI is restricted to work only when
the camera moves in 2D space and has not been applied on omni-cameras images so
far. In this work, we extend the iFMI method and apply a motion model to
estimate an omni-camera's pose when it moves in 3D space. This is particularly
useful in field robotics applications to get a rapid and comprehensive view of
unstructured environments, and to estimate robustly the robot pose. In the
experiment section, we compared the extended iFMI method against ORB and AKAZE
feature based approaches on three datasets showing different type of
environments: office, lawn and urban scenery (MPI-omni dataset). The results
show that our method boosts the accuracy of the robot pose estimation two to
four times with respect to the feature registration techniques, while offering
lower processing times. Furthermore, the iFMI approach presents the best
performance against motion blur typically present in mobile robotics.Comment: 5 pages, 4 figures, 1 tabl
A 3D Framework for Characterizing Microstructure Evolution of Li-Ion Batteries
Lithium-ion batteries are commonly found in many modern consumer devices, ranging from portable computers and mobile phones to hybrid- and fully-electric vehicles. While improving efficiencies and increasing reliabilities are of critical importance for increasing market adoption of the technology, research on these topics is, to date, largely restricted to empirical observations and computational simulations. In the present study, it is proposed to use the modern technique of X-ray microscopy to characterize a sample of commercial 18650 cylindrical Li-ion batteries in both their pristine and aged states. By coupling this approach with 3D and 4D data analysis techniques, the present study aimed to create a research framework for characterizing the microstructure evolution leading to capacity fade in a commercial battery. The results indicated the unique capabilities of the microscopy technique to observe the evolution of these batteries under aging conditions, successfully developing a workflow for future research studies
Coordinates and maps of the Apollo 17 landing site
We carried out an extensive cartographic analysis of the Apollo 17 landing site and determined and mapped positions of the astronauts, their equipment, and lunar landmarks with accuracies of better than ±1 m in most cases. To determine coordinates in a lunar bodyâfixed coordinate frame, we applied least squares (2âD) network adjustments to angular measurements made in astronaut imagery (Hasselblad frames). The measured angular networks were accurately tied to lunar landmarks provided by a 0.5 m/pixel, controlled Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) orthomosaic of the entire TaurusâLittrow Valley. Furthermore, by applying triangulation on measurements made in Hasselblad frames providing stereo views, we were able to relate individual instruments of the Apollo Lunar Surface Experiment Package (ALSEP) to specific features captured in LROC imagery and, also, to determine coordinates of astronaut equipment or other surface features not captured in the orbital images, for example, the deployed geophones and Explosive Packages (EPs) of the Lunar Seismic Profiling Experiment (LSPE) or the Lunar Roving Vehicle (LRV) at major sampling stops. Our results were integrated into a new LROC NACâbased Apollo 17 Traverse Map and also used to generate a series of largeâscale maps of all nine traverse stations and of the ALSEP area. In addition, we provide crater measurements, profiles of the navigated traverse paths, and improved ranges of the sources and receivers of the active seismic experiment LSPE
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
Cameras are a crucial exteroceptive sensor for self-driving cars as they are
low-cost and small, provide appearance information about the environment, and
work in various weather conditions. They can be used for multiple purposes such
as visual navigation and obstacle detection. We can use a surround multi-camera
system to cover the full 360-degree field-of-view around the car. In this way,
we avoid blind spots which can otherwise lead to accidents. To minimize the
number of cameras needed for surround perception, we utilize fisheye cameras.
Consequently, standard vision pipelines for 3D mapping, visual localization,
obstacle detection, etc. need to be adapted to take full advantage of the
availability of multiple cameras rather than treat each camera individually. In
addition, processing of fisheye images has to be supported. In this paper, we
describe the camera calibration and subsequent processing pipeline for
multi-fisheye-camera systems developed as part of the V-Charge project. This
project seeks to enable automated valet parking for self-driving cars. Our
pipeline is able to precisely calibrate multi-camera systems, build sparse 3D
maps for visual navigation, visually localize the car with respect to these
maps, generate accurate dense maps, as well as detect obstacles based on
real-time depth map extraction
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
- âŠ