262 research outputs found
Opaque voxel-based tree models for virtual laser scanning in forestry applications
Virtual laser scanning (VLS), the simulation of laser scanning in a computer environment, is a useful tool for field campaign planning, acquisition optimisation, and development and sensitivity analyses of algorithms in various disciplines including forestry research. One key to meaningful VLS is a suitable 3D representation of the objects of interest. For VLS of forests, the way trees are constructed influences both the performance and the realism of the simulations. In this contribution, we analyse how well VLS can reproduce scans of individual trees in a forest. Specifically, we examine how different voxel sizes used to create a virtual forest affect point cloud metrics (e.g., height percentiles) and tree metrics (e.g., tree height and crown base height) derived from simulated point clouds. The level of detail in the voxelisation is dependent on the voxel size, which influences the number of voxel cells of the model. A smaller voxel size (i.e., more voxels) increases the computational cost of laser scanning simulations but allows for more detail in the object representation. We present a method that decouples voxel grid resolution from final voxel cube size by scaling voxels to smaller cubes, whose surface area is proportional to estimated normalised local plant area density. Voxel models are created from terrestrial laser scanning point clouds and then virtually scanned in one airborne and one UAV-borne simulation scenario. Using a comprehensive dataset of spatially overlapping terrestrial, UAV-borne and airborne laser scanning field data, we compare metrics derived from simulated point clouds and from real reference point clouds. Compared to voxel cubes of fixed size with the same base grid size, using scaled voxels greatly improves the agreement of simulated and real point cloud metrics and tree metrics. This can be largely attributed to reduced artificial occlusion effects. The scaled voxels better represent gaps in the canopy, allowing for higher and more realistic crown penetration. Similarly high accuracy in the derived metrics can be achieved using regular fixed-sized voxel models with notably finer resolution, e.g., 0.02 m. But this can pose a computational limitation for running simulations over large forest plots due to the ca. 50 times higher number of filled voxels. We conclude that opaque scaled voxel models enable realistic laser scanning simulations in forests and avoid the high computational cost of small fixed-sized voxels
Martian impact craters: Continuing analysis of lobate ejecta sinuosity
The lobate ejecta morphology surrounding most fresh Martian impact craters can be quantitatively analyzed to determine variations in ejecta sinuosity with diameter, latitude, longitude, and terrain. The results of such studies provide another clue to the question of how these morphologies formed: are they the results of vaporization of subsurface volatiles or caused by ejecta entrainment in atmospheric gases. Kargel provided a simple expression to determine the degree of non-circularity of an ejecta blanket. This measure of sinuosity, called 'lobateness', is given by the ratio of the ejecta perimeter to the perimeter of a circle with the same area as that of the ejecta. The Kargel study of 538 rampart craters in selected areas of Mars led to the suggestion that lobateness increased with increasing diameter, decreased at higher latitude, and showed no dependence on elevation or geologic unit. Major problems with the Kargel analysis are the limited size and distribution of the data set and the lack of discrimination among the different types of lobate ejecta morphologies. Bridges and Barlow undertook a new lobateness study of 1582 single lobe (SL) and 251 double lobe (DL) craters. The results are summarized. These results agree with the finding of Kargel that lobateness increases with increasing diameter, but found no indication of a latitude dependence for SL craters. The Bridges and Barlow study has now been extended to multiple lobe (ML) craters. Three hundred and eighty ML craters located across the entire Martian surface were studied. ML craters provide more complications to lobateness studies than do SL and DL craters - in particular, the ejecta lobes surrounding the crater are often incomplete. Since the lobateness formula compares the perimeter of the ejecta lobe to that of a circle, the analysis was restricted only to complete lobes. The lobes are defined sequentially starting with the outermost lobe and moving inward
Mechanism of periodic height variations along self-aligned VLS-grown planar nanostructures
In this study we report in-plane nanotracks produced by molecular-beam-epitaxy (MBE) exhibiting lateral self-assembly and unusual periodic and out-of-phase height variations across their growth axes. The nanotracks are synthesized using bismuth segregation on the GaAsBi epitaxial surface, which results in metallic liquid droplets capable of catalyzing GaAsBi nanotrack growth via the vapor–liquid–solid (VLS) mechanism. A detailed examination of the nanotrack morphologies is carried out employing a combination of scanning electron and atomic force microscopy and, based on the findings, a geometric model of nanotrack growth during MBE is developed. Our results indicate diffusion and shadowing effects play significant roles in defining the interesting nanotrack shape. The unique periodicity of our lateral nanotracks originates from a rotating nucleation “hot spot” at the edge of the liquid–solid interface, a feature caused by the relative periodic circling of the non-normal ion beam flux incident on the sample surface, inside the MBE chamber. We point out that such a concept is divergent from current models of crawling mode growth kinetics and conclude that these effects may be utilized in the design and assembly of planar nanostructures with controlled non-monotonous structure
Exploring nanoscale characterization of low dimensional electronic materials
The advent of Atomic Force Microscopy (AFM) has allowed researchers to probe materials on the atomic scale with relative simplicity. For the study of nanoscale materials, structure is very important and often has a large impact on the materials intrinsic properties. The conventional form of Atomic Force Microscopy was developed to study material structure in the form of surface topography measurements. Since then there has been many advances which have taken advantage of the ability to detect small forces using an AFM tip along with surface topology.
A driving motivation in the scan probe microscopy field is the ability to spatially correlate properties of electronic materials such as charge density, conductivity, and doping distribution with nanoscale structure. Nanoscale characterization has become increasingly relevant as device features continue to shrink according to Moore’s Law leading to the advent of next generation electronic materials such as semiconducting nanowires and Carbon nanotubes. The primary issue with measuring nanoscale materials properties is that the tip-sample coulomb forces and quantum effects that provide insight into the material’s properties are very difficult to detect. A semiconducting Nanowire (NW) typically less than 500nm in diameter, is a quasi 1-dimensional structure with feature sizes approaching the diffraction limit of light rendering conventional optical spectroscopy ineffective; hence scan probe techniques are the most promising for characterization. Carbon Nanotubes, typically 1.0nm – 3.0nm in diameter, are 1-dimensional structures that are particularly difficult to characterize due to their infinitesimal sample volume. So far there has been very limited success electrically characterizing CNTs at the individual nanotube scale. Despite the challenges associated with nanomaterial characterization there have been successes at characterizing the electrical and chemical composition in parallel with morphology using capacitance sensitive AFM techniques. In this study I will describe and present data from AFM techniques with the ability to characterize semiconducting nanowires and carbon nanotubes.
In chapter 1, there is a review of several variations of capacitive AFM used to measure electrical properties and chemical properties of nanomaterials, some of which require specific sample preparation making them incompatible with nanotube and nanowire characterization. Next, in chapter 2, is an introduction to Microwave Impedance Microscopy (MIM), a novel nondestructive scan probe technique we offer as a viable alternative for low dimensional electronic material characterization. The goal of Chapter 3 is to demonstrate the ability to measure the quantum capacitance of individual CNTs using MIM illustrating it’s capability to measure nanoscale electrical phenomena. In chapter 4, MIM-AFM is used to provide insight into the structurally correlated doping dynamics of laterally grown GaAs nanowires. Finally, in chapter 5, a new scan probe technique called Near Field Infrared Microscopy (NFIR) is shown to be a complimentary characterization technique to MIM by probing the dopant distribution in GaAs nanowires. Many of the observations made using MIM-AFM and NFIR have never been seen before and could potentially have a high impact on nanowire device fabrication and characterization
Sensing Mountains
Sensing mountains by close-range and remote techniques is a challenging task. The 4th edition of the international Innsbruck Summer School of Alpine Research 2022 – Close-range Sensing Techniques in Alpine Terrain brings together early career and experienced scientists from technical-, geo- and environmental-related research fields. The interdisciplinary setting of the summer school creates a creative space for exchanging and learning new concepts and solutions for mapping, monitoring and quantifying mountain environments under ongoing conditions of change
Robust and accurate depth estimation by fusing LiDAR and Stereo
Depth estimation is one of the key technologies in some fields such as
autonomous driving and robot navigation. However, the traditional method of
using a single sensor is inevitably limited by the performance of the sensor.
Therefore, a precision and robust method for fusing the LiDAR and stereo
cameras is proposed. This method fully combines the advantages of the LiDAR and
stereo camera, which can retain the advantages of the high precision of the
LiDAR and the high resolution of images respectively. Compared with the
traditional stereo matching method, the texture of the object and lighting
conditions have less influence on the algorithm. Firstly, the depth of the
LiDAR data is converted to the disparity of the stereo camera. Because the
density of the LiDAR data is relatively sparse on the y-axis, the converted
disparity map is up-sampled using the interpolation method. Secondly, in order
to make full use of the precise disparity map, the disparity map and stereo
matching are fused to propagate the accurate disparity. Finally, the disparity
map is converted to the depth map. Moreover, the converted disparity map can
also increase the speed of the algorithm. We evaluate the proposed pipeline on
the KITTI benchmark. The experiment demonstrates that our algorithm has higher
accuracy than several classic methods
Estimating Autonomous Vehicle Localization Error Using 2D Geographic Information
Accurately and precisely knowing the location of the vehicle is a critical requirement for
safe and successful autonomous driving. Recent studies suggest that error for map-based localization
methods are tightly coupled with the surrounding environment. Considering this relationship, it
is therefore possible to estimate localization error by quantifying the representation and layout of
real-world phenomena. To date, existing work on estimating localization error have been limited
to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D
geographic information datasets as a proxy to estimate autonomous vehicle localization error. Seven
map evaluation factors were defined for 2D geographic information in a vector format, and random
forest regression was used to estimate localization error for five experiment paths in Shinjuku, Tokyo.
In the best model, the results show that it is possible to estimate autonomous vehicle localization
error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm
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