130 research outputs found
Locating clustered seismicity using Distance Geometry Solvers: applications for sparse and single-borehole DAS networks
The determination of seismic event locations with sparse networks or
single-borehole systems remains a significant challenge in observational
seismology. Leveraging the advantages of the location approach HADES, which was
initially developed for locating clustered seismicity recorded at two stations,
we present here an improved version of the methodology: HADES-R. Where HADES
previously needed a minimum of 4 absolutely located master events, HADES-R
solves a least-squares problem to find the relative inter-event distances in
the cluster, and uses only a single master event to find the locations of all
events, and subsequently applies rotational optimiser to find the cluster
orientation. It can leverage iterative station combinations if multiple
receivers are available, to describe the cluster shape and orientation
uncertainty with a bootstrap approach. The improved method requires P- and
S-phase arrival picks, a homogeneous velocity model, a single master event with
a known location, and an estimate of the cluster width. The approach is
benchmarked on the 2019 Ridgecrest sequence recorded at two stations, and
applied to two seismic clusters at the FORGE geothermal test site, including a
microseismic monitoring scenario with a DAS in a vertical borehole. Traditional
procedures struggle in these settings due to the ill-posed network
configuration. The azimuthal ambiguity in this scenario is partially overcome
by assuming that all events belong to the same cluster around the master event
and a cluster width estimate. We find the cluster shape in both cases, although
the orientation remains uncertain. The method's ability to constrain the
cluster shape and location with only one well-located event offers promising
implications, especially for environments where limited or specialised
instrumentation is in use.Comment: 33 pages, 15 figures. Manuscript submitted to Geophysical Journal
Internationa
Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption
In this paper, we present a novel pose normalization method for indoor
mapping point clouds and triangle meshes that is robust against large fractions
of the indoor mapping geometries deviating from an ideal Manhattan World
structure. In the case of building structures that contain multiple Manhattan
World systems, the dominant Manhattan World structure supported by the largest
fraction of geometries is determined and used for alignment. In a first step, a
vertical alignment orienting a chosen axis to be orthogonal to horizontal floor
and ceiling surfaces is conducted. Subsequently, a rotation around the
resulting vertical axis is determined that aligns the dataset horizontally with
the coordinate axes. The proposed method is evaluated quantitatively against
several publicly available indoor mapping datasets. Our implementation of the
proposed procedure along with code for reproducing the evaluation will be made
available to the public upon acceptance for publication
Appearance and Geometry Assisted Visual Navigation in Urban Areas
Navigation is a fundamental task for mobile robots in applications such as exploration, surveillance, and search and rescue. The task involves solving the simultaneous localization and mapping (SLAM) problem, where a map of the environment is constructed. In order for this map to be useful for a given application, a suitable scene representation needs to be defined that allows spatial information sharing between robots and also between humans and robots. High-level scene representations have the benefit of being more robust and having higher exchangeability for interpretation. With the aim of higher level scene representation, in this work we explore high-level landmarks and their usage using geometric and appearance information to assist mobile robot navigation in urban areas.
In visual SLAM, image registration is a key problem. While feature-based methods such as scale-invariant feature transform (SIFT) matching are popular, they do not utilize appearance information as a whole and will suffer from low-resolution images. We study appearance-based methods and propose a scale-space integrated Lucas-Kanade’s method that can estimate geometric transformations and also take into account image appearance with different resolutions. We compare our method against state-of-the-art methods and show that our method can register images efficiently with high accuracy.
In urban areas, planar building facades (PBFs) are basic components of the quasirectilinear environment. Hence, segmentation and mapping of PBFs can increase a robot’s abilities of scene understanding and localization. We propose a vision-based PBF segmentation and mapping technique that combines both appearance and geometric constraints to segment out planar regions. Then, geometric constraints such as reprojection errors, orientation constraints, and coplanarity constraints are used in an optimization process to improve the mapping of PBFs.
A major issue in monocular visual SLAM is scale drift. While depth sensors, such as lidar, are free from scale drift, this type of sensors are usually more expensive compared to cameras. To enable low-cost mobile robots equipped with monocular cameras to obtain accurate position information, we use a 2D lidar map to rectify imprecise visual SLAM results using planar structures. We propose a two-step optimization approach assisted by a penalty function to improve on low-quality local minima results.
Robot paths for navigation can be either automatically generated by a motion planning algorithm or provided by a human. In both cases, a scene representation of the environment, i.e., a map, is useful to specify meaningful tasks for the robot. However, SLAM results usually produce a sparse scene representation that consists of low-level landmarks, such as point clouds, which are neither convenient nor intuitive to use for task specification. We present a system that allows users to program mobile robots using high-level landmarks from appearance data
Subsurface structure of the Solfatara volcano (Campi Flegreicaldera, Italy) as deduced from joint seismic-noise array,volcanological and morphostructural analysis
The joint application of different seismological techniques for seismic noise analysis, and the results of a volcanological and morphostructural survey, have allowed us to obtain a detailed and well constrained image of the shallow crustal structure of the Solfatara volcano (Campi Flegrei caldera, Italy). Horizontal-to-vertical spectral ratios, inversion of surface wave dispersion curves and polarization analysis provided resonance frequencies and peak amplitudes, shear wave velocity profiles and polarization pattern of coherent ambient noise. These results, combined in a unique framework, indicate that the volcanic edifice is characterized by lateral and vertical discontinuities and heterogeneities in terms of shear wave velocity, lithological contrasts and structural setting. The interpretation of the seismological results, with the volcanological and morphostructural constraints, supports the hypothesis that the volcano has been characterized by a complex and intense activity, with the alternation of constructive and destructive phases, during which magmatic and phreatomagmatic explosions built a complex tuff-cone, later reworked by atmospheric agents and altered by hydrothermal activity. The differences in the velocity structure between the central and eastern parts of the crater have been interpreted as resulting from a possible eastward migration of the eruptive vent along the deformational features affecting the area, and to the presence of viscous lava and lithified tuff bodies within the feeding conduits, which are buried under a covering of reworked materials of variable thickness. The observed fault and fracture systems, partially inherited from regional structural setting and exhumed during volcanism and ground deformation episodes also seems to strongly control wave propagation, affecting the noise polarization properties
Effect of coherent noise on single-station direction of arrival estimation
Polarization analysis of multi-component seismic data is used in both exploration seismology and earthquake seismology. In single-station polarization processing, it is generally assumed that any noise present in the window of analysis is incoherent, i.e., does not correlate between components. This assumption is often violated in practice because several overlapping seismic events may be present in the data. The additional arrival(s) to that of interest can be viewed as coherent noise. This paper quantifies the error because of coherent noise interference. We first give a general theoretical analysis of the problem. A simple mathematical wavelet is then used to obtain a closed-form solution to the principal direction estimated for a transient incident signal superposed with a time-shifted, unequal amplitude version of itself, arriving at an arbitrary angle to the first wavelet. The effects of relative amplitude, arrival angle, and the time delay of the two wavelets on directional estimates are investigated. Even for small differences in angle of arrival, there may be significant error (>10°) in the azimuth estimat
Simple fish-eye calibration method with accuracy evaluation
In this paper, a simple fish-eye radial distortion calibration procedure is described. This method avoids costly minimisation and optimisation algorithms, and is based on trivial concentricity of three extracted points. The results show that this simplicity is at the expense of increased deviation of results (and thus increased error). However, this deviation can be reduced significantly by the use of simple averaging, such that it is only marginally greater than the current state-of-the-art
A Universal Slope Set for 1-Bend Planar Drawings
We describe a set of Delta-1 slopes that are universal for 1-bend planar drawings of planar graphs of maximum degree Delta>=4; this establishes a new upper bound of Delta-1 on the 1-bend planar slope number. By universal we mean that every planar graph of degree Delta has a planar drawing with at most one bend per edge and such that the slopes of the segments forming the edges belong to the given set of slopes. This improves over previous results in two ways: Firstly, the best previously known upper bound for the 1-bend planar slope number was 3/2(Delta-1) (the known lower bound being 3/4(Delta-1)); secondly, all the known algorithms to construct 1-bend planar drawings with O(Delta) slopes use a different set of slopes for each graph and can have bad angular resolution, while our algorithm uses a universal set of slopes, which also guarantees that the minimum angle between any two edges incident to a vertex is pi/(Delta-1)
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