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Localization and Mapping Systems In article number 2200459, Dongjiao He, Wei Xu, and colleagues present a high‐bandwidth LiDAR‐inertial system (LIO), which updates the state at the sampling time of each LiDAR point or IMU measurement without accumulating a frame. The system is motion distortion free and is able to output a high‐rate (4 kHz – 8 kHz), high‐bandwidth (over 150 Hz) odometry and handles extremely aggressive motions where IMU saturates.

The transparency of the atmosphere affects the quality of astronomical observations and optical communications, but above all, it directly controls the fluxes of radiation, which is of particular interest for the study of weather and climate changes. The transparency of the atmosphere also affects the success of observing thermospheric lidar reflections. Since they are a consequence of the high transparency of the atmosphere, the reflections can be used as characteristics of atmospheric transparency.

In this paper we present a description of a new multispectral airborne mapping light detection and ranging (lidar) along with performance results obtained from two years of data collection and test campaigns. The Titan multiwave lidar is manufactured by Teledyne Optech Inc. (Toronto, ON, Canada) and emits laser pulses in the 1550, 1064 and 532 nm wavelengths simultaneously through a single oscillating mirror scanner at pulse repetition frequencies (PRF) that range from 50 to 300 kHz per wavelength (max combined PRF of 900 kHz). The Titan system can perform simultaneous mapping in terrestrial and very shallow water environments and its multispectral capability enables new applications, such as the production of false color active imagery derived from the lidar return intensities and the automated classification of target and land covers. Field tests and mapping projects performed over the past two years demonstrate capabilities to classify five land covers in urban environments with an accuracy of 90%, map bathymetry under more than 15 m of water, and map thick vegetation canopies at sub-meter vertical resolutions. In addition to its multispectral and performance characteristics, the Titan system is designed with several redundancies and diversity schemes that have proven to be beneficial for both operations and the improvement of data quality.

Impacts of small‐scale surface irregularities, or surface roughness, of atmospheric ice crystals on lidar backscattering properties are quantified. Geometric ice crystal models with various degrees of surface roughness and state‐of‐the‐science light‐scattering computational capabilities are utilized to simulate the single‐scattering properties across the entire practical size parameter range. The simulated bulk lidar and depolarization ratios of polydisperse ice crystals at wavelength 532 nm are strongly sensitive to the degree of surface roughness. Comparisons of these quantities between the theoretical simulations and counterparts inferred from spaceborne lidar observations for cold cirrus clouds suggest a typical surface‐roughness‐degree range of 0.03–0.15 in the cases of compact hexagonal ice crystals, which is most consistent with direct measurements of scanning electron microscopic images. To properly interpret lidar backscattering observations of ice clouds, it is necessary to account for the degree of surface roughness in light‐scattering computations involving ice crystals. Plain Language Summary Lidar (Light Detection and Ranging) instruments on satellites use reflected, or backscattered, laser beams to investigate ice clouds in the atmosphere. However, it has long been a challenge to interpret lidar signals, called backscattering properties, to infer ice cloud characteristics accurately. This study uses theoretical simulations to investigate how small‐scale surface irregularities of ice crystals affect the lidar signals associated with ice clouds. These simulations demonstrate the significant impacts of small‐scale surface irregularities of ice crystals on backscattering. Based on comparisons between the theoretical simulations and satellite lidar observations, it is necessary to assume a moderate degree of small‐scale surface irregularities to explain lidar observations of typical ice clouds. Key Points The sensitivity of the backscattering properties to the surface roughness of atmospheric ice crystals is theoretically investigated The depolarization ratio is substantially sensitive to the degree of surface roughness of ice crystals Compact hexagonal ice models with degrees of surface roughness ranging 0.03–0.15 reasonably explain the Cloud‐Aerosol Lidar with Orthogonal Polarization backscattering signals

Nacelle‐mounted, forward‐facing light detection and ranging (LIDAR) technology can deliver benefits to rotor speed regulation and loading reductions of floating offshore wind turbines (FOWTs) when assisting with blade pitch control in above‐rated wind speed conditions. Large‐scale wind turbines may be subject to significant variations in structural loads due to differences in the wind profile across the rotor‐swept area. These loading fluctuations can be mitigated by individual pitch control (IPC). This paper presents a novel LIDAR‐assisted feedforward IPC approach that uses each blade's rotor azimuth position to allocate an individual pitch command from a multi‐beam LIDAR. In this study, the source code of OpenFAST wind turbine modelling software was modified to enable LIDAR simulation and LIDAR‐assisted control. The LIDAR simulation modifications were accepted by the National Renewable Energy Laboratory (NREL) and are now present within OpenFAST releases from v3.5 onwards. Simulations of a 15 MW FOWT were performed across the above‐rated wind spectrum. Under a turbulent wind field with an average wind speed of 17 ms−1, the LIDAR‐assisted feedforward IPC delivered up to 54% reductions in the root mean squared errors and standard deviations of key FOWT parameters. Feedforward IPC delivered enhancements of up to 12% over feedforward collective pitch control, relative to the baseline feedback controller. The reductions to the standard deviation and range of the rotor speed may enable structural optimization of the tower, while the reductions in the variations of the loadings present an opportunity for reduced fatigue damage on turbine components and, consequently, a reduction in maintenance expenditure.

P208

Autonomous driving requires robust and highly accurate perception technologies. Various deep learning algorithms based on only image processing satisfy this requirement, but few such algorithms are based on LiDAR. However, images are only one part of the perceptible sensors in an autonomous driving vehicle; LiDAR is also essential for the recognition of driving environments. The main reason why there exist few deep learning algorithms based on LiDAR is a lack of data. Recent translation technology using generative adversarial networks (GANs) has been proposed to deal with this problem. However, these technologies focus on only image-to-image translation, although a lack of data occurs more often with LiDAR than with images. LiDAR translation technology is required not only for data augmentation, but also for driving simulation, which allows algorithms to practice driving as if they were commanding a real vehicle, before doing so in the real world. In other words, driving simulation is a key technology for evaluating and verifying algorithms which are practically applied to vehicles. In this paper, we propose a GAN-based LiDAR translation algorithm for autonomous driving and driving simulation. It is the first LiDAR translation approach that can deal with various types of weather that are based on an empirical approach. We tested the proposed method on the JARI data set, which was collected under various adverse weather scenarios with diverse precipitation and visible distance settings. The proposed method was also applied to the real-world Spain data set. Our experimental results demonstrate that the proposed method can generate realistic LiDAR data under adverse weather conditions.

P232