Volumetric assessment of hepatic grafts using a light detection and ranging system for 3D scanning:Preliminary data

BACKGROUND Liver transplantation has evolved into a safe life-saving operation and remains the golden standard in the treatment of end stage liver disease.The main limiting factor in the application of liver transplantation is graft shortage.Many strategies have been developed in order to alleviate graft shortage,such as living donor partial liver transplantation and split liver transplantation for adult and pediatric patients.In these strategies,liver volume assessment is of paramount importance,as size mismatch can have severe consequences in the success of liver transplantation.AIM To evaluate the safety,feasibility,and accuracy of light detection and ranging(LIDAR)3D photography in the prediction of whole liver graft volume and mass.METHODS Seven liver grafts procured for orthotopic liver transplantation from brain deceased donors were prospectively measured with an LIDAR handheld camera and their mass was calculated and compared to their actual weight.RESULTS The mean error of all measurements was 17.03 g(range 3.56-59.33 g).Statistical analysis of the data yielded a Pearson correlation coefficient index of 0.9968,indicating a strong correlation between the values and a Student’s t-test P value of 0.26.Mean accuracy of the measurements was calculated at 97.88%.CONCLUSION Our preliminary data indicate that LIDAR scanning of liver grafts is a safe,cost-effective,and feasible method of ex vivo determination of whole liver volume and mass.More data are needed to determine the precision and accuracy of this method.

Multi-Scale Feature Extraction for Joint Classification of Hyperspectral and LiDAR Data

With the development of sensors,the application of multi-source remote sensing data has been widely concerned.Since hyperspectral image(HSI)contains rich spectral information while light detection and ranging(LiDAR)data contains elevation information,joint use of them for ground object classification can yield positive results,especially by building deep networks.Fortu-nately,multi-scale deep networks allow to expand the receptive fields of convolution without causing the computational and training problems associated with simply adding more network layers.In this work,a multi-scale feature fusion network is proposed for the joint classification of HSI and LiDAR data.First,we design a multi-scale spatial feature extraction module with cross-channel connections,by which spatial information of HSI data and elevation information of LiDAR data are extracted and fused.In addition,a multi-scale spectral feature extraction module is employed to extract the multi-scale spectral features of HSI data.Finally,joint multi-scale features are obtained by weighting and concatenation operations and then fed into the classifier.To verify the effective-ness of the proposed network,experiments are carried out on the MUUFL Gulfport and Trento datasets.The experimental results demonstrate that the classification performance of the proposed method is superior to that of other state-of-the-art methods.

Intelligent Risk-Identification Algorithm with Vision and 3D LiDAR Patterns at Damaged Buildings

Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study aims to increase the efficiency of search and rescue operations and the safety offirefigh-ters by detecting and identifying the disaster site by recognizing collapsed areas,obstacles,and rescuers on-site.A fusion algorithm combining a camera and three-dimension light detection and ranging(3D LiDAR)is proposed to detect and loca-lize the interiors of disaster sites.The algorithm detects obstacles by analyzingfloor segmentation and edge patterns using a mask regional convolutional neural network(mask R-CNN)features model based on the visual data collected from a parallelly connected camera and 3D LiDAR.People as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring rescue.The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and estimate the distance to the actual object using the center point of the clustering result.The proposed artificial intelligence(AI)algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detectfloor surfaces,atypical obstacles,and persons requiring rescue.Accordingly,the fused AI algorithm was comparatively verified.

Three-channel CMOS transimpedance amplifier for LiDAR sensor receiver

For time-of-flight(TOF)light detection and ranging(LiDAR),a three-channel high-performance transimpedance amplifier(TIA)with high immunity to input load capacitance is presented.A regulated cascade(RGC)as the input stage is at the core of the complementary metal oxide semiconductor(CMOS)circuit chip,giving it more immunity to input photodiode detectors.A simple smart output interface acting as a feedback structure,which is rarely found in other designs,reduces the chip size and power consumption simultaneously.The circuit is designed using a 0.5μm CMOS process technology to achieve low cost.The device delivers a 33.87 dB?transimpedance gain at 350 MHz.With a higher input load capacitance,it shows a-3 dB bandwidth of 461 MHz,indicating a better detector tolerance at the front end of the system.Under a 3.3 V supply voltage,the device consumes 5.2 mW,and the total chip area with three channels is 402.8×597.0μm2(including the test pads).

Predicting the provisioning potential of forest ecosystem services using airborne laser scanning data and forest resource maps

Background: Remote sensing-based mapping of forest Ecosystem Service(ES) indicators has become increasingly popular. The resulting maps may enable to spatially assess the provisioning potential of ESs and prioritize the land use in subsequent decision analyses. However, the mapping is often based on readily available data, such as land cover maps and other publicly available databases, and ignoring the related uncertainties.Methods: This study tested the potential to improve the robustness of the decisions by means of local model fitting and uncertainty analysis. The quality of forest land use prioritization was evaluated under two different decision support models: either using the developed models deterministically or in corporation with the uncertainties of the models.Results: Prediction models based on Airborne Laser Scanning(ALS) data explained the variation in proxies of the suitability of forest plots for maintaining biodiversity, producing timber, storing carbon, or providing recreational uses(berry picking and visual amenity) with RMSEs of 15%–30%, depending on the ES. The RMSEs of the ALS-based predictions were 47%–97%of those derived from forest resource maps with a similar resolution. Due to applying a similar field calibration step on both of the data sources, the difference can be attributed to the better ability of ALS to explain the variation in the ES proxies.Conclusions: Despite the different accuracies, proxy values predicted by both the data sources could be used for a pixel-based prioritization of land use at a resolution of 250 m~2, i.e., in a considerably more detailed scale than required by current operational forest management. The uncertainty analysis indicated that maps of the ES provisioning potential should be prepared separately based on expected and extreme outcomes of the ES proxy models to fully describe the production possibilities of the landscape under the uncertainties in the models.

An evaluation of the role played by remote sensing technology following the World Trade Center attack

Remote sensing technology has been widely recognized for contributing to emergency response efforts after the World Trade Center attack on September 11th, 2001. The need to coordinate activities in the midst of a dense, yet relatively small area, made the combination of imagery and mapped data strategically useful. This paper reviews the role played by aerial photography, satellite imagery, and LIDAR data at Ground Zero. It examines how emergency managers utilized these datasets, and identifies significant problems that were encountered. It goes on to explore additional ways in which imagery could have been used, while presenting recommendations for more effective use in future disasters and Homeland Security applications. To plan adequately for future events, it was important to capture knowledge from individuals who responded to the World Trade Center attack. In recognition, interviews with key emergency management and geographic information system (GIS) personnel provide the basis of this paper. Successful techniques should not be forgotten, or serious problems dismissed. Although widely used after September 11th, it is important to recognize that with better planning, remote sensing and GIS could have played an even greater role. Together with a data acquisition timeline, an expanded discussion of these issues is available in the MCEER/NSF report “Emergency Response in the Wake of the World Trade Center Attack; The Remote Sensing Perspective” (Huyck and Adams, 2002) Keywords World Trade Center (WTC) - terrorism - emergency response - emergency management - ground zero - remote sensing - emergency operations - disasters - geographic information systems (GIS) - satellite imagery - synthetic aperture radar (SAR) - light detection and ranging imagery (LIDAR)

Mapping tree canopies in urban environments using airborne laser scanning (ALS):a Vancouver case study

Background: The distribution of forest vegetation within urban environments is critically important as it influences urban environmental conditions and the energy exchange through the absorption of solar radiation and modulation of evapotranspiration. It also plays an important role filtering urban water systems and reducing storm water runoff.Methods: We investigate the capacity of ALS data to individually detect, map and characterize large(taller than15 m) trees within the City of Vancouver. Large trees are critical for the function and character of Vancouver’s urban forest. We used an object-based approach for individual tree detection and segmentation to determine tree locations(position of the stem), to delineate the shape of the crowns and to categorize the latter either as coniferous or deciduous.Results: Results indicate a detection rate of 76.6% for trees > 15 m with a positioning error of 2.11 m(stem location). Extracted tree heights possessed a RMSE of 2.60 m and a bias of-1.87 m, whereas crown diameter was derived with a RMSE of 3.85 m and a bias of-2.06 m. Missed trees are principally a result of undetected treetops occurring in dense, overlapping canopies with more accurate detection and delineation of trees in open areas.Conclusion: By identifying key structural trees across Vancouver’s urban forests, we can better understand their role in providing ecosystem goods and services for city residents.

On the potential to predetermine dominant tree species based on sparse-density airborne laser scanning data for improving subsequent predictions of species-specific timber volumes

Background： Tree species recognition is the main bottleneck in remote sensing based inventories aiming to produce an input for species-specific growth and yield models. We hypothesized that a stratification of the target data according to the dominant species could improve the subsequent predictions of species-specific attributes in particular in study areas strongly dominated by certain species. Methods： We tested this hypothesis and an operational potential to improve the predictions of timber volumes, stratified to Scots pine, Norway spruce and deciduous trees, in a conifer forest dominated by the pine species. We derived predictor features from airborne laser scanning （ALS） data and used Most Similar Neighbor （MSN） and Seemingly Unrelated Regression （SUR） as examples of non-parametric and parametric prediction methods, respectively Results： The relationships between the ALS features and the volumes of the aforementioned species were considerably different depending on the dominant species. Incorporating the observed dominant species inthe predictions improved the root mean squared errors by 13.3-16.4 % and 12.6-28.9 % based on MSN and SUR, respectively, depending on the species. Predicting the dominant species based on a linear discriminant analysis had an overall accuracy of only 76 % at best, which degraded the accuracies of the predicted volumes. Consequently, the predictions that did not consider the dominant species were more accurate than those refined with the predicted species. The MSN method gave slightly better results than models fitted with SUR. Conclusions： According to our results, incorporating information on the dominant species has a clear potential to improve the subsequent predictions of species-specific forest attributes. Determining the dominant species based solely on ALS data is deemed challenging, but important in particular in areas where the species composition is otherwise seemingly homogeneous except being dominated by certain species.

Next Generation Scanning LIDAR Systems for Optimizing Wake Turbulence Separation Minima

Numerous studies have been performed to better understand the behavior of wake vortices with regards to aircraft characteristics and weather conditionsover the pastten years. These studies have led to the development of the aircraft RECATegorization(RECAT) programs in Europe and in USA. Its phase one focused on redefining distance separation matrix with six static aircraft wake turbulence categories instead of three with the current International Civil Aviation Organization(ICAO) regulations. In Europe, the RECAT-EU regulation is now entering under operational implementation atseveral key airports. As proven by several research projects in the past, LIght Detection And Ranging(LIDAR) sensors are considered as the ground truth wake vortex measurements for assessing the safety impact of a new wake turbulence regulation at an airport in quantifying the risks given the local specificities. LIDAR's can also be used to perform risk monitoring after the implementation. In this paper, the principle to measure wake vortices with scanning coherent Doppler LIDARs is described as well as its dedicated post-processing. Finally the use of WINDCUBELIDAR based solution for supporting the implementation of new wake turbulenceregulation is described along with satisfyingresults that have permitted the monitoring of the wake vortex encounter risk after the implementation of a new wake turbulence regulation.

Effects of laser beam divergence angle on airborne LIDAR positioning errors

The influence of laser beam divergence angle on the positioning accuracy of scanning airborne light detection and ranging （LIDAR） is analyzed and simulated. Based on the data process and positioning principle of airborne LIDAR, the errors from pulse broadening induced by laser beam di vergence angle are modeled and qualitatively analyzed for different terrain surfaces. Simulated results of positioning errors and suggestions to reduce them are given for the flat surface, the downhill of slope surface, and the uphill surface.

《激光雷达测量技术与应用》课程教学设计与思考

在工程教育专业认证背景下,本文针对《激光雷达技术与应用》课程在以往的课程教学设计中存在的重理论轻实训、灌输式课堂教学、课程考核评价形式单一等问题进行了课程层面的教学设计模式改革探索,实现了教学目标、教学内容、教学方法、教学评价等环节与OBE教学理念的高度融合。实践证明,本课程教学设计改革成效显著,培养了学生自主创新学习意识和工程意识,提高了学生解决复杂工程问题的能力。本文旨在为地方转型高校基层专业教师,在具体课程教学中,如何准确把握、贯彻工程教育基本内涵和标准提供借鉴。

Novel robust simultaneous localization and mapping for long-term autonomous robots

A fundamental task for mobile robots is simultaneous localization and mapping(SLAM).Moreover,long-term robustness is an important property for SLAM.When vehicles or robots steer fast or steer in certain scenarios,such as low-texture environments,long corridors,tunnels,or other duplicated structural environments,most SLAM systems might fail.In this paper,we propose a novel robust visual inertial light detection and ranging(Li Da R)navigation(VILN)SLAM system,including stereo visual-inertial Li Da R odometry and visual-Li Da R loop closure.The proposed VILN SLAM system can perform well with low drift after long-term experiments,even when the Li Da R or visual measurements are degraded occasionally in complex scenes.Extensive experimental results show that the robustness has been greatly improved in various scenarios compared to state-of-the-art SLAM systems.

UAV Laser scanning technology:a potential cost-effective tool for micro-topography detection over wooded areas for archaeological prospection

Airborne laser scanning(ALS)has recently been identified as a potential tool in topographic mapping for archaeological prospection.However,most existing applications in this field refers to manned ALS systems,for which the high operation and maintenance costs limits its application in small-scale archaeological investigation.In this paper,we conducted an exploratory study on the application of the unmanned aerial vehicle(UAV)laser scanning(ULS)system in ancient micro-topography detection over wooded areas.Compared with manned ALS technology,we analyzed the advantages and potentials of ULS technology for archaeological applications.Then we outlined existing mainstream survey-grade UAV-based laser scanners,data processing and visualization approaches.Furthermore,we performed case studies in three cultural heritage sites in Zhejiang Province,China using two representative mainstream survey-grade ULS systems.Results were then verified by an in-site investigation.Finally,the correct selection of ULS devices,the planning of data acquisition missions and the use of appropriate data processing methods specifically for archaeological prospection were discussed.This paper provides a cost-effective and flexible solution for micro-topography detection in wooded areas.ULS technology,as demonstrated here,can be an important supplement to existing archaeological investigation methods,particularly for small-scale areas,and has promising prospects in archaeological applications.

Multi-Object Tracking Strategy of Autonomous Vehicle Using Modified Unscented Kalman Filter and Reference Point Switching

In this study,a multi-object tracking(MOT)scheme based on a light detection and ranging sensor was proposed to overcome imprecise velocity observations in object occlusion scenarios.By applying real-time velocity estimation,a modified unscented Kalman filter(UKF)was proposed for the state estimation of a target object.The proposed method can reduce the calculation cost by obviating unscented transformations.Additionally,combined with the advantages of a two-reference-point selection scheme based on a center point and a corner point,a reference point switching approach was introduced to improve tracking accuracy and consistency.The state estimation capability of the proposed UKF was verified by comparing it with the standard UKF in single-target tracking simulations.Moreover,the performance of the proposed MOT system was evaluated using real traffic datasets.

Overcoming the limitations of 3D sensors with wide field of view metasurface-enhanced scanning lidar

Lidar, a technology at the heart of autonomous driving and robotic mobility, performs 3D imaging ofa complex scene by measuring the time of flight of returning light pulses. Many technological challenges,including enhancement of the observation field of view (FoV), acceleration of the imaging frame rate,improvement of the ambiguity range, reduction of fabrication cost, and component size, must besimultaneously addressed so that lidar technology reaches the performance needed to strongly impact theglobal market. We propose an innovative solution to address the problem of wide FoV and extendedunambiguous range using an acousto-optic modulator that rapidly scans a large-area metasurface deflector.We further exploit a multiplexing illumination technique traditionally deployed in the context of telecommu-nication theory to extend the ambiguity range and to drastically improve the signal-to-noise ratio of themeasured signal. Compacting our metasurface-scanning lidar system to chip-scale dimension would opennew and exciting perspectives, eventually relevant to the autonomous vehicles and robotic industries.

一种基于全等三角形的点云自动配准方法

点云配准是点云数据处理中比较关键的步骤,直接影响处理结果。经典的迭代最近点(iterative closest point,ICP)算法需要目标点云与源点云之间有良好的初始姿态,否则会遇到局部最优等问题。因此,提出了一种基于全等三角形的点云自动配准方法。该方法通过边长相等和面积相等来构造全等三角形,找到源点云与目标点云的对应点,建立源点云与目标点云的对应关系和转换参数最优估计,完成粗配准,再结合ICP算法进行精配准,实现点云的自动配准。结果表明,与四点一致集(4-points congruent sets,4PCS)配准算法和ICP算法的结合算法相比,所提算法能有效改善ICP算法对初值依赖的问题,并且配准精度有一定提升。

Rapid development methodology of agricultural robot navigation system working in GNSS-denied environment

Robotic autonomous operating systems in global n40avigation satellite system(GNSS)-denied agricultural environments(green houses,feeding farms,and under canopy)have recently become a research hotspot.3D light detection and ranging(LiDAR)locates the robot depending on environment and has become a popular perception sensor to navigate agricultural robots.A rapid development methodology of a 3D LiDAR-based navigation system for agricultural robots is proposed in this study,which includes:(i)individual plant clustering and its location estimation method(improved Euclidean clustering algorithm);(ii)robot path planning and tracking control method(Lyapunov direct method);(iii)construction of a robot-LiDAR-plant unified virtual simulation environment(combination use of Gazebo and SolidWorks);and(vi)evaluating the accuracy of the navigation system(triple evaluation:virtual simulation test,physical simulation test,and field test).Applying the proposed methodology,a navigation system for a grape field operation robot has been developed.The virtual simulation test,physical simulation test with GNSS as ground truth,and field test with path tracer showed that the robot could travel along the planned path quickly and smoothly.The maximum and mean absolute errors of path tracking are 2.72 cm,1.02 cm;3.12 cm,1.31 cm,respectively,which meet the accuracy requirements of field operations,establishing the effectiveness of the proposed methodology.The proposed methodology has good scalability and can be implemented in a wide variety of field robot,which is promising to shorten the development cycle of agricultural robot navigation system working in GNSS-denied environment.

数字航空影像与机载点云的配准参数优化及对应数据集构建

数字航空影像和机载点云之间的配准参数精度会直接影响到配准效果,利用共线方程及影像特征点和点云特征点之间计算相似性测度的方法进行参数优化,有效避免了由于初始参数误差导致的配准偏差。首先,提取航空影像及激光雷达(light detection and ranging,LiDAR)点云的特征点;然后,根据初始配准参数及距离误差计算影像与点云之间的匹配点对;最后,通过强制搜索(brute-force,BF)优化方法来寻找更加精确的匹配参数。此外,还构建了2D-3D对应区域的数据集,用于航空影像和机载LiDAR数据配准的相关研究。

Intelligent optimization of seam-line finding for orthophoto mosaicking with LiDAR point clouds

A detailed study was carried out to find optimal seam-lines for mosaicking of images acquired by an airborne light detection and ranging (LiDAR) system.High ground objects labeled as obstacles can be identified by delineating black holes from filtered point clouds obtained by filtering the raw laser scanning dataset.An innovative A algorithm is proposed that can automatically make the seam-lines keep away from these obstacles in the registered images.This method can intelligently optimize the selection of seam-lines and improve the quality of orthophotos.A simulated grid image was first used to analyze the effect of different heuristic functions on path planning.Three subsets of LiDAR data from Xi'an,Dunhuang,and Changyang in Northwest China were obtained.A quantitative method including pixel intensity,hue,and texture was used.With our proposed method,9.4%,8.7%,and 9.8% improvements were achieved in Dunhuang,Xi'an,and Changyang,respectively.

Feedforward Control for Wind Turbine Load Reduction with Pseudo-LIDAR Measurement

A gain-scheduled feedforward controller, based on pseudo-LIDAR （light detection and ranging） wind speed measurement, is designed to augment the baseline feedback controller for wind turbine＇s load reduction in above rated operation. The pseudo-LIDAR measurement data are generated from a commercial software- Bladed using a designed sampling strategy. The nonlinear wind turbine model has been simplified and linearised at a set of equilibrium operating points. The feedforward controller is firstly developed based on a linearised model at an above rated wind speed, and then expanded to the full above rated operational envelope by employing gain scheduling strategy. The combined feedforward and baseline feedback control is simulated on a 5MW industrial wind turbine model. Simulation studies demonstrate that the proposed control strategy can improve the rotor and tower load reduction performance for large wind turbines.