二次散射对脉冲激光雷达回波功率的影响

理论上分析了二次散射对大气脉冲激光雷达回波功率的影响并提出一种计算方法。采用某型大气激光雷达的测试结果与理论计算具有较强的一致性,证明了理论结果的正确性。根据二次散射回波信号的特点,选用适当的滤波方法将其影响降至最低。二次散射的理论计算解释了实测中发现的＂纹波＂现象,纹波的幅度和频率与雷达自身特性及粒子特性相关,其衰减速度较单次散射慢得多。因此对于大气探测具有进一步研究的重要价值。     

基于IMU与激光雷达紧耦合的混合匹配算法

针对线、面特征匹配的激光雷达测距与地图构建算法(Lightweight and Ground-Optimized Lidar Odometry And Mapping,LeGO-LOAM)在自动导引运输车(Automated Guided Vehicle,AGV)室内室外实时建图与定位时,易出现激光里程计累积误差大和旋转估计不准确等问题,本工作采用惯性测量单元(Inertial Measurement Unit,IMU)与激光雷达紧耦合的LeGO-LOAM算法,通过IMU为激光雷达提供的初始位姿信息,构建IMU与激光雷达联合误差函数,实现位姿共同迭代优化.其中,对于室外结构化信息较少时,在点对点的迭代最近点算法(Iterative Closest Point,ICP)较高定位精度的基础上,结合LeGO-LOAM算法和ICP算法互补性,进一步提出基于IMU与激光雷达紧耦合的混合匹配算法:当环境中结构信息较多时,激光里程计采用LeGO-LOAM算法,而当环境中结构化信息较少时采用ICP算法.实验结果表明,基于IMU与激光雷达紧耦合的混合匹配算法可有效降低激光里程计相对位姿误差和累积误差,提高AGV小车定位精度以消除部分地图重影.     

面向车路协同的路侧感知仿真系统

路侧感知是车路协同应用开发的重要组成部分,通过在路侧部署传感器,将采集到的路面信息经V2X通信给到车辆,使车辆拥有超视距的感知能力.在实际应用中,为达到最优的路侧感知效果,不同的场景往往需要不同的RSU配置,RSU的选型及安装是一个耗时耗力的过程.交通参与者的识别是路侧感知的核心,基于机器学习的识别算法需要大量的标签数据,而人工打标签被验证是一个效率极其低下的方式.通过构建路侧感知仿真系统可以很好地解决RSU配置及样本数据生成的问题,实验一通过在仿真系统中调整激光雷达的高度和角度,得到极端情况下的车辆遮挡情况,从而为激光雷达的实际安装高度提供参考,实验二在仿真环境中输出带标签的激光雷达点云数据,通过与实际采集的点云数据进行融合对比,验证仿真系统输出的激光雷达点云数据可以作为模型训练的数据补充.     

Geomorphic comparison of two Atlantic coastal rivers: Toward an understanding of physical controls on Atlantic salmon habitat

River channel substrate size and mobility are important to Atlantic salmon spawning and rearing success. We compare morphology and bed sediment between two North American Atlantic coastal streams (Narraguagus River, Maine, USA and Jacquet River, New Brunswick, Canada). The watersheds have similar drainage areas and mean annual precipitation, but differing relief structure, channel longitudinal profiles and numbers of returning salmon. The lower‐relief Narraguagus River is segmented into steeper (gradient >0.002) and flatter reaches (gradient <0.0005). Flat reaches, including mainstem lakes, act as sediment sinks, preventing the continuity of downstream sediment transport. Based on field measurements, the Narraguagus River has a larger high‐flow width to depth ratio than the Jacquet River, but this difference is principally the result of outliers from low‐gradient channel reaches. Measurements of substrate grain size reveal finer river‐bed sediments on the Narraguagus River, however, Shields parameter calculations indicate that bed sediment should be mobile during high flows in both streams. We use the Shields equation to predict grain size based on channel slope, width and drainage area measured from digital elevation models (DEM) and aerial photographs. Predictions of median grain size agree with field measurements within a factor of 2 for 62–70% of the survey stations. We suggest ways that model misfits may provide opportunities to prioritize reach‐based restoration efforts. Based on expected grain size, we estimate 62% spawning and 68% rearing habitat along the length of the Narraguagus River, and 28 and 95% respectively on the Jacquet. Overall, glacial history and relief structure appear to be the first‐order controls on substrate grain size and habitat quality in these two rivers. Copyright © 2010 John Wiley & Sons, Ltd.     

激光雷达的多源数据融合点云分类算法研究

根据单档输电线空间分布特性,提出了改进随机采样一致的输电线点云分割方法。首先优化初始样本点选择原则、引入最小二乘原理参数求解等改进策略,提高了随机采样一致性算法输电线模型重建精度;然后以直线-抛物线方程为单根输电线识别的约束条件,利用逐根提取方式实现输电线激光点云分割。选择两组典型代表性的机载激光点云数据进行实验分析,该方法有效解决了数据缺失、点云噪声等复杂背景环境的输电线激光点云分割,准确率、召回率和整体精度最小值分别为99.19%、99.25%、99.10%。较之已有方法,本文方法具有点云分割精度高、算法普适性强的优势;随机采样一致性(RANSAC)算法是常见的激光点云分割方法,但该算法推广至输电线场景时存在点云分割效率低、抗噪性差等不足,不利于高精度的输电线模型重建及后续线路风险检测。     

山地地理测绘勘探中反射式激光雷达回波特征优化

地理测绘勘探激光雷达回波特征在山地区域应用,会出现光斑回波叠加,导致坐标混乱,特征监测效果差。本研究结合反射式激光雷达,在采集山地地理特征光斑数据的基础上,优化设计反射式激光雷达在山地地理测绘勘探中的应用过程,并确定其相关参数。通过分区对山地地理一一扫描,整合扫描结果,采集该山地地理特征的激光雷达回波数据。设计一种光斑回波波形分解模型,通过分解处理获取地理特征,去除光斑回波叠加干扰,在通过坐标和高程转换,解决坐标混乱问题,完成特征的监控。实验结果表明：该方法与传统的方法比较,能够更加有效监测出山地地理的变形量,监测效果好、监测性能较高。     

一种激光雷达探测模块控制电路的设计和制作

传统上使用机械旋钮调节光电倍增管 (PMT) 增益的方法不仅存在需要人为依据经验手动操作、准确性差等 弊端, 而且 PMT 增益易受温度影响, 需要根据环境温度动态调整 PMT 的高压, 这些局限性都不利于其在激光雷达系 统中的应用。为了便于对 PMT 进行增益调节并保持 PMT 增益的稳定, 设计了可用于激光雷达系统的 PMT 控制电路 板, 该电路可使用计算机实现增益调节和高压的温度自适应调节, 从而精简了信号探测系统的结构与体积, 提升了信 号的稳定性。本工作采用内置高压电源的 H10721-20 型 PMT, 并基于 STM32 单片机结合数模转换器 (DAC) 和外围 电路完成控制电路板的设计并进行实际制作。进一步对使用电位器调节的 PMT 和利用所提出的控制方法进行调节 的 PMT 进行了高压稳定性的对比实验, 并对使用 PMT 控制电路板的米散射激光雷达的性能进行了测试。实验结果 表明所提出的控制方法可实现更稳定的 PMT 增益控制, 设计的 PMT 控制电路板具有良好的可靠性。     

Comparison of bathymetry and seagrass mapping with hyperspectral imagery and airborne bathymetric lidar in a shallow estuarine environment

We compared hyperspectral imagery and single-wavelength airborne bathymetric light detection and ranging (lidar) for shallow water (<2 m) bathymetry and seagrass mapping. Both the bathymetric results from hyperspectral imagery and airborne bathymetric lidar reveal that the presence of a strongly reflecting benthic layer under seagrass affects the elevation estimates towards the bottom depth instead of the top of seagrass canopy. Full waveform lidar was also investigated for bathymetry and similar performance to discrete lidar was observed. A provisional classification was performed with limited ground reference samples and four supervised classifiers were applied in the study to investigate the capability of airborne bathymetric lidar and hyperspectral imagery to identify seagrass genera. The overall classification accuracy is highly variable and strongly dependent on the classification strategy used. Features from bathymetric lidar alone are not sufficient for substrate classification, while hyperspectral imagery alone showed significant capability for substrate classification with over 95% overall accuracy. The fusion of hyperspectral imagery and bathymetric lidar only marginally improved the overall accuracy of seagrass classification.     

Assessment of the impacts of surface topography, off-nadir pointing and vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model

In order to prioritize the measurement requirements and accuracies of the two new lidar missions, a physical model is required for a fundamental understanding of the impact of surface topography, footprint size and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval. In this study, we extended a well developed Geometric Optical and Radiative Transfer (GORT) vegetation lidar model to take into account for the impacts of surface topography and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval and applied this extended model to assess the aforementioned impacts on vegetation lidar waveforms and height retrieval.Model simulation shows that surface topography and off-nadir pointing angle stretch waveforms and the stretching effect magnifies with footprint size, slope and off-nadir pointing angle. For an off-nadir pointing laser penetrating vegetation over a slope terrain, the waveform is either stretched or compressed based on the relative angle. The stretching effect also results in a disappearing ground peak return when slope or off-nadir pointing angle is larger than the “critical slope angle”, which is closely related to various vegetation structures and footprint size. Model simulation indicates that waveform shapes are affected by surface topography, off-nadir pointing angle and vegetation structure and it is difficult to remove topography effects from waveform extent based only on the shapes of waveform without knowing any surface topography information.Height error without correction of surface topography and off-nadir pointing angle is the smallest when the laser beams at the toward-slope direction and the largest from the opposite direction. Further simulation reveals within 20° of slope and off-nadir pointing angle, given the canopy height as roughly 25 m and the footprint size as 25 m, the error for vegetation height (RH100) ranges from − 2 m to greater than 12 m, and the error for the height at the medium energy return (RH50) from − 1 m to 4 m. The RH100 error caused by unknown surface topography and without correction of off-nadir pointing effect can be explained by an analytical formula as a function of vegetation height, surface topography, off-nadir pointing angle and footprint size as a first order approximation. RH50 is not much affected by topography, off-nadir pointing and footprint size. This forward model simulation can provide scientific guidance on prioritizing future lidar mission measurement requirements and accuracies.     

Physically based vertical vegetation structure retrieval from ICESat data: Validation using LVIS in White Mountain National Forest, New Hampshire, USA

Vegetation structure retrieval accuracies from spaceborne Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat) data are affected by surface topography, background noise and sensor saturation. This study uses a physical approach to remove surface topography effect from lidar returns to retrieve vegetation height from ICESat/GLAS data over slope terrains. Slope-corrected vegetation heights from ICESat/GLAS data were compared to airborne Laser Vegetation Imaging Sensor (LVIS) (20 m footprint size) and small-footprint lidar data collected in White Mountain National Forest, NH. Impact of slope on LVIS vegetation height estimates was assessed by comparing LVIS height before and after slope correction with small-footprint discrete-return lidar and field data.Slope-corrected GLAS vegetation heights match well with 98 percentile heights from small-footprint lidar (R² = 0.77, RMSE = 2.2 m) and top three LVIS mean (slope-corrected) heights (R² = 0.64, RMSE = 3.7 m). Impact of slope on LVIS heights is small, however, comparison of LVIS heights (without slope correction) with either small footprint lidar or field data indicates that our scheme improves the overall LVIS height accuracy by 0.4-0.7 m in this region. Vegetation height can be overestimated by 3 m over a 15° slope without slope correction. More importantly, both slope-corrected GLAS and LVIS height differences are independent of slope. Our results demonstrate the effectiveness of the physical approach to remove surface topography from large footprint lidar data to improve accuracy of maximum vegetation height estimates.GLAS waveforms were compared to aggregated LVIS waveforms in Bartlett Experimental Forest, NH, to evaluate the impact of background noise and sensor saturation on vegetation structure retrievals from ICESat/GLAS. We found that GLAS waveforms with sensor saturation and low background noise match well with aggregated LVIS waveforms, indicating these waveforms capture vertical vegetation structure well. However, waveforms with large noise often lead to mismatched waveforms with LVIS and underestimation of waveform extent and vegetation height. These results demonstrate the quality of ICESat/GLAS vegetation structure estimates.