This PDF file contains the front matter associated with SPIE Proceedings Volume 12110, including the Title Page, Copyright information, Table of Contents, and Committee Page.

LIght Detection And Ranging (LIDAR) systems are complex instruments whose performance is affected by a variety of atmospheric, system design, and geometric factors. To ensure performance parameters, such as maximum range and accuracy, are achieved it is imperative that statistically representative variations in these factors are considered to identify if the system will not meet performance goals under particular corner cases. To this end, a high fidelity, physics-based simulator was developed to aid in the LIDAR design process. This simulator is composed of four major libraries – atmosphere, LIDAR, electronics, and algorithms. The physics-based models in each of these libraries account for environmental, optical, electrical, and mechanical variations in the underlying components which ultimately affect LIDAR signals and data products. The goal of this simulator is to generate realistic LIDAR signals and LIDAR-derived data products to aid in performing trade studies, determine performance envelops, identify parameter tolerances, debug experimental data collections, and provide a testbed to evaluate different algorithms. The simulator has undergone validation and verification against other relevant models, such as NRL-MSISe00, LEEDR, MODTRAN, etc., as well as experimental data. Using this simulator, Monte Carlo techniques were utilized for aerosol (elastic), temperature (inelastic rotational Raman), and water vapor (inelastic vibrational-rotational Raman) LIDARs to determine their performance in a variety of representative atmospheres, and pointing angles. System performance is expressed as a cumulative distribution function of error between LIDAR-retrieved atmospheric quantities (aerosol extinction, temperature, and water vapor) and ‘truth’ atmospheric quantities input into the simulator.

Surface dust blown by a lunar lander can spoof sensors and damage lander and other surface and orbital assets. Since many countries seek to use and leverage the Moon in the coming decades, this is potentially a defense issue. Empirical data on Plume Surface Interactions (PSI) from lander-mounted instruments are needed to determine particle size distributions. We report a feasibility study of laser light-scattering for particle sizing. Calculations suggest that distributions of particle sizes in the range 0.1 to 10 microns can be accurately determined from laser-propagation decay using 4 to 8 wavelengths between 0.4 to 2 microns. Lab standards have been created based on calibrated showers of silica spheres and known concentrations and sizes of SiC grit in resin rods. Experiments were performed using lasers from 0.4 to 10 micron wavelength. For visible wavelengths, a point Si detector or images taken with a Si CCD camera were used to record scattered intensity vs propagation distance. At long-wave infrared, a pyroelectric detector or bolometer array were used. Characteristic decay lengths were determined by an algebraic sliding aperture method suitable for rapid and automated analysis. The experiments confirm theoretical expectations for Mie scattering by simple distributions of spherical particles. These results inform future experiments for testing the inverse problem of extracting more complicated size distributions from decay lengths measured using multiple wavelengths.

We benchmark long range ToF LiDAR with laser emitter at 905nm vs. 1550nm. Based on IEC eye safety standards, a high-power laser diode at 1550nm with 100W peak power can deliver 80 times more photon count compared to 905nm out of a LiDAR emitter. Considering detection distance, target reflectivity, and atmospheric media loss, a LiDAR with such power output at 1550nm can outperform 905nm by more than 60 times in SNR and 24 times higher in detection probability at a distance longer than 200m. At longer range of 250m, a single 1550nm emitter can function nicely in both clear sky and low visibility conditions with 20% target reflectivity. To achieve such high performance at 1550nm an innovative multi-junction laser structure has been developed. We present a triple junction high-power laser diode at 1550nm based on AlInGaAs/InP materials. The laser stacks three AlInGaAs lasers epitaxially connected by two tunnel junctions and grown on InP substrate. The monolithic laser structure with tunnel junction layers is designed in a way to reduce the stress and improve the heat dissipation. Each tunnel junctions is formed with an n-type InGaAs layer and a ptype InGaAs layer. The active area of each junction comprises AlInGaAs barrier and quantum well layers. The design leads to three times the output power of a single junction laser and reaches 1W/A slope efficiency. Over 100W peak optical power at 100A with a 350m aperture and 10 nsec pulse width is demonstrated. A low operating voltage is achieved with such triple junction design, thus the wall-plug efficiency is two times better. Given the required detection distance over 200m, a long-range LiDAR with a triple-junction 1550nm laser diode may enable high-speed autonomous vehicles with confidence.

Linear mode global shutter flash LIDAR (gsf-LIDAR) is addressing the need for 3D sensing in a wide range of space, airborne, autonomous vehicle, and marine applications. Flash LIDAR produces real time dense 3D point clouds, enriched with scene intensity information, that are not subject to motion distortion. Current and future applications require extended range performance from a low Size Weight and Power (SWaP) 3D sensor. A Linear Mode LIDAR with photon sensitivity comparable to Geiger Mode sensing is needed to meet the challenges. Geiger mode APDs sensors require high repetition rate lasers to allow multiple frame summing and the associated processing overhead creates a significant SWaP burden. Advanced Scientific Concepts LLC has developed a linear mode gsf-LIDAR camera which has significant improvements in the minimum photon detection threshold, 10X for a single frame and over 30X with the aid of frame summing, which offers long range performance with low flux photon counting capability. ASC 3D Flash LIDAR uncooled testing demonstrated sub 30 photon detection with a low repetition rate laser. This performance improvement enables low SWaP sensors for enhanced space-based LIDAR capabilities for rendezvous /docking and planetary landing hazard detection and avoidance applications.

Silicon avalanche photodiodes (Si APD) in linear mode operation have been used in spaceborne lidars around Mars, Mercury, Earth, and the Moon over the past 25 years. We have been monitoring the space radiation damages to some of these Si APDs over their mission lifetimes. It was found that the Si APD performance degradation depended on the location of the Si APDs inside the lidars and the thickness of the shielding materials around them. The major accumulated radiation effect was found to be the increase of the dark currents. Most of the radiation damage remained after weeks of annealing at room temperature. Heating the devices at high temperature could anneal the radiation damage to certain extent. Recently, the lidar on the Global Ecosystem Dynamics Investigation (GEDI) mission captured many individual pulse waveforms from transient radiation events from the Si APDs. We subsequently conducted a single event effects (SEE) test of a GEDI Engineering Model Si APD using 64 MeV protons to reproduce the anomalous pulse waveforms observed from GEDI in orbit. In this paper, we will present a summary of the Si APD performance monitored from several space lidars developed by and operated at NASA Goddard Space Flight Center. We will also present the laboratory test data from the recent SEE tests.

In recent years, lidar-based remote sensing has been used for detecting and classifying flying insects, which is based upon the fact that oscillating wings produce a modulated return signal; oscillations from other objects, such as helicopters or drones, might also be detected in a similar manner. Several groups have successfully used machine learning to classify insects in laboratory settings, but data processing in field studies is still performed manually. Compared to laboratory studies, field studies pose additional challenges, such as non-stationary background clutter and high class imbalance. The models we used for detection and classification were the common boosting algorithm AdaBoost, a hybrid sampling/boosting algorithm RUSBoost, and a neural network with a single hidden layer. Previously, we found that the best performances came from the neural network and AdaBoost. In this paper, we test the machine learning models that have been trained on field data collected from Hyalite Creek on other unlabeled field data; in doing so, we demonstrate each model’s ability to detect insects in data from new, unseen environments. We Use labels created by a domain expert to manually check how many of the predicted images actually contained insects.

Photon-sensitive lidar receivers enable range measurements at high probability of detection and low false alarm rate using only 5 - 10 detected photons on average per range measurement. This much-reduced link requirement, compared to photodiodes operating in linear mode, holds the promise of much-reduced system volume, mass, and power consumption, while simultaneously enabling longer standoff and higher measurement rates. We present a commercially-available, Geiger-mode lidar system, called Zion, optimized for rapid collection of dense 3D point clouds using small, economical aircraft. The system mass is under 120 kg and it consumes under 1 kW. Zion has operated at ranges between 800 m and 8,000 m. The area collection rate for data products with density of 100 points per square meter exceeds 300 km²/hr at an aircraft altitude of 1,400 m. The maximum usable measurement rate exceeds 10 million points per second. A significant capability of Zion is the agile geo-referenced scanning system, which can point and scan anywhere within a 40 × 40 degree field of regard. Collection efficiency is optimized by scanning only the desired geographic region of interest (e.g. meandering roads and utility corridors) and even in spite of non-ideal aircraft flight path and attitude. The agile, georeferenced scanning allows the flexibility to maximize oblique imaging of structures or to penetrate dense foliage. The collected points are spread evenly across the imaged area, which reduces image artifacts and simplifies processing. This system has flown over 50 flights, and is currently operational.

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), on-orbit for nearly 3 years, continues to provide global elevation measurements to the broad science community. One of the most transformative discoveries during early mission operations was the realization that ICESat-2 could provide bathymetry in addition to the planned surface-specific data products for land ice, sea ice, ocean and land/vegetation. This is an important capability for coastal science, maritime intelligence and shallow water benthic monitoring at the global scale. ICESat-2 elevations are also becoming a critical component of satellite-derived bathymetry where multispectral imagery uses the measurements to create broad spatial maps with absolute vertical bathymetric depths. This article will highlight some of the most salient bathymetric observations and quantitative analyses of this space-based photon counting lidar that includes sea floor elevation retrievals but also environmental characterization such as wave structure and turbidity and monitoring of benthic habitats.

Aerial lidar systems tend to have narrow instantaneous fields of view, with imagers ranging from a single pixel to many tens of thousands of pixels. To collect data over a large area, the narrow lidar field of view (FoV) must be scanned. We present a unique method of scanning a lidar FoV that provides significant flexibility and allows uniform ground coverage, concentrating the system capability only in areas of interest. This method uses a queue of convex polygons, specified in world coordinates. Pre-collection planning tools establish the polygon layout. In flight, the lidar system adaptively collects those polygons that are inside the sensor field of regard, rapidly switching among the polygons as the aircraft flies. This scanning method enables the lidar to accomplish repeated collections of a single target or collections that cover a long straight or meandering path. It also enables collection of corridors with irregular widths, such as power line corridors with bulges at municipal power sub-stations or rail or roadway intersections. In the case of mixed scene types, the system can concentrate more collection time on foliated regions relative to unfoliated regions. Angular diversity can be achieved by sequentially revisiting a single target polygon. Live target tasking is accomplished by adding new targets to the target queue without stopping an ongoing collection. We present scanning simulations and example lidar data collected in flight with this scanning strategy and show some examples of sampling uniformity under the finite bandwidth and acceleration of a real scanning system.

Different types of 3D sensors, such as LiDAR and RGB-D cameras, capture data with different resolution, range, and noise characteristics. It is often desired to merge these different types of data together into a coherent scene, but automatic alignment algorithms generally assume that the characteristics of each fragment are all similar. Our goal is to evaluate the performance of these algorithms on data with different characteristics to enable the integration of data from multiple types of sensors.

We use the Redwood dataset, which has high-resolution scans of several different environments captured using a stationary LiDAR scanner. We first develop a method to emulate the capture of these environments as viewed by different types of sensor by leveraging OpenGL and a mesh creation process. Next, we take fragments of these captures which represent scenarios in which each type of sensor would be used, using our scanning experience to inform the selection process. Finally, we attempt to merge the fragments together using several automatic algorithms and evaluate how the results compare with the original scenes. We evaluate based on transformation similarity to ground truth, algorithm speed and ease of use, and subjective quality assessments.

The interplay between laser beam divergence and receiver field of view in airborne bathymetric LiDAR has a strong influence on spatial resolution, depth performance when measuring into the water, and vegetation penetration. Spatial resolution is ultimately limited by the laser footprint’s size on the ground, so a low beam divergence is usually preferable. Under certain circumstances, the capability of measuring through vegetation to the ground may be improved by using a larger laser beam divergence. The receiver field of view is usually kept as small as possible in order not to collect an unnecessary amount of background radiation. For bathymetric laser scanning, a large field of view enables the receiver to pick up more of the laser signal scattered on its way through the water column. In this paper, we systematically investigate the qualitative and quantitative effects when varying both parameters by operating a RIEGL VQ-840-G airborne topo-bathymetric laser scanner in different altitudes and with different parameter settings over waterbodies and forest areas.

In this work we raise the question of noise suppression by statistical analysis of Analog-to-digital converter (ADC) data vs. Time-to-digital converter (TDC) sampled data. The current technology options are identified. We analyze realistic scenarios through computer simulation and conclude that TDC has the same statistical potential as ADC, but current devices offer insufficient memory depth for long range, low signal to noise ratio laser range-finders’ applications.