Low-power optical beam steering by microelectromechanical waveguide gratings

Optical beam steering is key for optical communications, laser mapping (LIDAR), and medical imaging. For these applications, integrated photonics is an enabling technology that can provide miniaturized, lighter, lower cost, and more power efficient systems. However, common integrated photonic devices are too power demanding. Here, we experimentally demonstrate, for the first time, beam steering by microelectromechanical (MEMS) actuation of a suspended silicon photonic waveguide grating. Our device shows up to 5.6{\deg} beam steering with 20 V actuation and a power consumption below the $\mu$W level, i.e. more than 5 orders of magnitude lower power consumption than previous thermo-optic tuning methods. The novel combination of MEMS with integrated photonics presented in this work lays ground for the next generation of power-efficient optical beam steering systems

Classification and Change Detection in Mobile Mapping LiDAR Point Clouds

Creating 3D models of the static environment is an important task for the advancement of driver assistance systems and autonomous driving. In this work, a static reference map is created from a Mobile Mapping “light detection and ranging” (LiDAR) dataset. The data was obtained in 14 measurement runs from March to October 2017 in Hannover and consists in total of about 15 billion points. The point cloud data are first segmented by region growing and then processed by a random forest classification, which divides the segments into the five static classes (“facade”, “pole”, “fence”, “traffic sign”, and “vegetation”) and three dynamic classes (“vehicle”, “bicycle”, “person”) with an overall accuracy of 94%. All static objects are entered into a voxel grid, to compare different measurement epochs directly. In the next step, the classified voxels are combined with the result of a visibility analysis. Therefore, we use a ray tracing algorithm to detect traversed voxels and differentiate between empty space and occlusion. Each voxel is classified as suitable for the static reference map or not by its object class and its occupation state during different epochs. Thereby, we avoid to eliminate static voxels which were occluded in some of the measurement runs (e.g. parts of a building occluded by a tree). However, segments that are only temporarily present and connected to static objects, such as scaffolds or awnings on buildings, are not included in the reference map. Overall, the combination of the classification with the subsequent entry of the classes into a voxel grid provides good and useful results that can be updated by including new measurement data

Improving models for environmental applications of LiDAR: Novel approaches based on soft computing

This work proposes novel methodologies to improve the use of Light Detection And Ranging (LiDAR) for environ mental purposes, especially for thematic mapping (LiDAR only or fused with other remote sensors) and the estimation of for est variables. The methodologies make use of well-known techniques from soft computing (machine learning and evolutionary computation) and their adaptation to develop LiDAR-derived products.Ministerio de Educación y Ciencia TIN2007-68084-C-02-01Ministerio de Ciencia e Innovación TIN2011-28956-C02-02Junta de Andalucía TIC-752

3D Reconstruction & Assessment Framework based on affordable 2D Lidar

Lidar is extensively used in the industry and mass-market. Due to its measurement accuracy and insensitivity to illumination compared to cameras, It is applied onto a broad range of applications, like geodetic engineering, self driving cars or virtual reality. But the 3D Lidar with multi-beam is very expensive, and the massive measurements data can not be fully leveraged on some constrained platforms. The purpose of this paper is to explore the possibility of using cheap 2D Lidar off-the-shelf, to preform complex 3D Reconstruction, moreover, the generated 3D map quality is evaluated by our proposed metrics at the end. The 3D map is constructed in two ways, one way in which the scan is performed at known positions with an external rotary axis at another plane. The other way, in which the 2D Lidar for mapping and another 2D Lidar for localization are placed on a trolley, the trolley is pushed on the ground arbitrarily. The generated maps by different approaches are converted to octomaps uniformly before the evaluation. The similarity and difference between two maps will be evaluated by the proposed metrics thoroughly. The whole mapping system is composed of several modular components. A 3D bracket was made for assembling of the Lidar with a long range, the driver and the motor together. A cover platform made for the IMU and 2D Lidar with a shorter range but high accuracy. The software is stacked up in different ROS packages.Comment: 7 pages, 9 Postscript figures. Accepted by 2018 IEEE International Conference on Advanced Intelligent Mechatronic

Multi-staged Research at the Denmark Site, A Small Early-Middle Mississippian Town

Early-Middle Mississippian settlements in the hinterlands of West Tennessee have largely gone unstudied. The void in settlement data leaves a gap in understanding Early-Middle Mississippian settlements within the Mid-South region. A multi-staged research design at the Denmark Site (40MD85) in Madison County, Tennessee was employed to determine a settlement system at Denmark. Denmark was originally thought to be a Vacant Mound Center that did not support an associated habitation, but topographic mapping, LiDAR data, magnetometry survey, and targeted excavation reveal that the Denmark mound group represents a sizeable settlement

Comparison of 16-Channel Laser Photoreceivers for Topographic Mapping

Topographic mapping lidar instruments must be able to detect extremely weak laser return signals from high altitudes including orbital distance. The signals have a wide dynamic range caused by the variability in atmospheric transmission and surface reflectance under a fast moving spacecraft. Ideally, lidar detectors should be able to detect laser signal return pulses at the single photon level and produce linear output for multiple photon events. Silicon avalanche photodiode (APO) detectors have been used in most space lidar receivers to date. Their sensitivity is typically hundreds of photons per pulse, and is limited by the quantum efficiency, APO gain noise, dark current, and preamplifier noise. NASA is pursuing three approaches for a 16-channel laser photoreceiver for use on the next generation direct-detection airborne and spacebome lidars. We present our measurement results and a comparison of their performance

Evaporite karst geohazards in the Delaware Basin, Texas: review of traditional karst studies coupled with geophysical and remote sensing characterization

Evaporite karst throughout the Gypsum Plain of west Texas is complex and extensive, including manifestations ranging from intrastratal brecciation and hypogene caves to epigene features and suffosion caves. Recent advances in hydrocarbon exploration and extraction has resulted in increased infrastructure development and utilization in the area; as a result, delineation and characterization of potential karst geohazards throughout the region have become a greater concern. While traditional karst surveys are essential for delineating the subsurface extent and morphology of individual caves for speleogenetic interpretation, these methods tend to underestimate the total extent of karst development and require surficial manifestation of karst phenomena. Therefore, this study utilizes a composite suite of remote sensing and traditional field studies for improved karst delineation and detection of potential karst geohazards within gypsum karst. Color InfraRed (CIR) imagery were utilized for delineation of lineaments associated with fractures, while Normalized Density Vegetation Index (NDVI) analyses were used to delineate regions of increased moisture flux and probable zones of shallow karst development. Digital Elevation Models (DEM) constructed from high-resolution LiDAR (Light Detection and Ranging) data were used to spatially interpret sinkholes, while analyses of LiDAR intensity data were used in a novel way to categorize local variations in surface geology. Resistivity data, including both direct current (DC) and capacitively coupled (CC) resistivity analyses, were acquired and interpreted throughout the study area to delineate potential shallow karst geohazards specifically associated with roadways of geohazard concern; however, detailed knowledge of the surrounding geology and local karst development proved essential for proper interpretation of resistivity inversions. The composite suite of traditional field investigations and remotely sensed karst delineations used in this study illustrate how complex gypsum karst terrains can be characterized with greater detail through the utilization of rapidly advancing technologies, especially in arid environments with low vegetation densities