Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.

基于IMU-LiDAR紧耦合的煤矿防冲钻孔机器人定位导航方法

防冲钻孔机器人是冲击地压矿井卸压的关键设备,其在复杂卸压巷道的精确地图构建和的稳定导航是实现钻孔作业智能化的基础和前提。在分析激光雷达点云畸变成因和同步定位与地图构建(SLAM)算法缺陷的基础上,设计了基于惯性测量单元(IMU)连续时间轨迹的点云畸变矫正方法,建立了激光雷达和IMU的数据融合模型,提出了基于IMU-LiDAR紧耦合的防冲钻孔机器人定位建图方法。根据煤矿卸压巷道特点建立了密闭坡道模型,开展了建图效果仿真分析,结果表明,所提算法在定位精度、轨迹误差方面均优于现有常用算法。在此基础上,设计了基于改进人工势场法和快速扩展随机树的动态路径规划方法,建立了适用于防冲钻孔机器人的路径规划与导航融合方案,并设计了2种仿真运动场景,结果表明,所提路径规划方法在全局路径规划和动态路径规划的平均路径长度、平均运行时间、平均生成节点数等方面均具有较好的综合性能。为了进一步验证防冲钻孔机器人定位导航方法的实用性,在校内模拟巷道、地面实验基地和井下卸压巷道等场景下开展了多组对比实验,结果表明:将IMU数据与LiDAR数据紧耦合后,所提方法的定位建图精度明显提高,在特征退化场景中具有优越的定位建图性能,且规划路径的运算效率和路径代价方面均具有良好的表现,验证了所提定位导航方法在多种场景中的可行性和优越性。

Dual-Path Vision Transformer用于急性缺血性脑卒中辅助诊断

急性缺血性脑卒中是由于脑组织血液供应障碍导致的脑功能障碍,数字减影脑血管造影(DSA)是诊断脑血管疾病的金标准。基于患者的正面和侧面DSA图像,对急性缺血性脑卒中的治疗效果进行分级评估,构建基于Vision Transformer的双路径图像分类智能模型DPVF。为了提高辅助诊断速度,基于EdgeViT的轻量化设计思想进行了模型的构建;为了使模型保持轻量化的同时具有较高的精度,提出空间-通道自注意力模块,促进Transformer模型捕获更全面的特征信息,提高模型的表达能力;此外,对于DPVF的两分支的特征融合,构建交叉注意力模块对两分支输出进行交叉融合,促使模型提取更丰富的特征,从而提高模型表现。实验结果显示DPVF在测试集上的准确率达98.5%,满足实际需求。

基于LiDAR数据与光谱影像融合的单木提取方法

针对现有的机载数据单木分割方法对林型的普适度不高,尤其在高郁闭度阔叶林地带提取精度偏低的问题,选用海南省海口市热带阔叶林地带的光谱影像和LiDAR数据,先采用基于距离阈值的单木分割方法,利用高分光谱影像分割得到的树冠边缘,对初始探测树顶点进行位置约束。获得单木顶点的精确定位后,采用基于种子点的单木分割方法分割,完成了阔叶林的单木提取。结果显示,与已有的基于单木间相对间距单木分割方法相比,本研究通过选取最佳分割尺度结合光谱影像进行精确定位,改善了原有单一尺度分割方法导致的过分割现象,将单木识别精确率由0.67提升至0.92。该方法在使用遥感对森林单木进行分割工作中,可以更好地识别单木,对不同林型适用度较高,可以为后续的单木信息提取工作提供数据基础。

融合LiDAR点云与无人机影像的滑坡动态监测技术

滑坡是一种危害性较大的自然灾害,如何对其进行高效准确的监测具有重要研究价值和实际意义。利用LiDAR、无人机航空摄影等技术进行滑坡监测,可以快速、安全、精确地获取滑坡区域地面信息。本文提出了融合LiDAR点云的无人机影像滑坡动态监测方法。首先,利用点云和影像获取高质量DSM;然后,设计一种基于不规则三角网和坡度融合的滤波算法,滤除DSM中低矮植被,生产高精度DEM;最后,通过对两期DEM进行差分,实现对滑坡区域的动态监测。以黄登水电站附近边坡区域的LiDAR数据与无人机影像数据开展试验,结果表明,采用本文方法进行滑坡动态监测可以直观地判断滑坡地形变化和位移趋势,具有一定的应用前景。

多源LiDAR和UAV影像的塔式建筑物三维建模方法

塔式建筑物由于立面结构复杂,使用单一数据源进行三维建模时容易出现部分区域空洞、纹理拉花等情况。为解决此问题,本文提出一种将无人机(Unmanned Aerial Vehicle,UAV)影像、机载LiDAR数据和手持LiDAR数据进行配准融合的塔式建筑物三维建模方法。以云南省玉溪市新平县的五彩云楼为例,使用UAV和手持激光扫描仪作为数据采集设备,首先分别采集塔式建筑物的UAV影像、机载LiDAR和手持LiDAR数据,然后基于摄影测量原理生成UAV影像的点云,其次基于最近点迭代算法方法将三种数据进行配准融合,最后通过构建不规则三角网表示其三维模型。实验结果表明:将多源LiDAR和UAV影像点云配准融合后生成的三维模型结构更加完整,避免了单一数据源构建三维模型存在的空洞问题。

3D稀疏卷积结构下融合空间点与体素关系建模的LiDAR点云跟踪方法

稀疏卷积在处理激光雷达点云单目标跟踪时的潜力尚未得到充分发掘.目前,绝大多数点云跟踪算法使用基于球邻域的骨干网络,其显存计算资源占用大并且目标感知的关系建模不充分.针对此问题,本文提出一种基于稀疏卷积结构的LiDAR(Lightlaser Detection And Ranging)点云跟踪算法,并创新性地融合了空间点与体素双通道的关系建模模块,以高效适应稀疏框架下目标判别信息的嵌入.首先,本文采用3D稀疏卷积残差网络来分别提取模板和搜索区域的特征,并利用反卷积来获取逐点特征来保证跟踪任务中对空间位置特性的要求.其次,关系建模模块进一步在模板与搜索区域特征之间计算相似度语义查询表.为了捕捉到模板与搜索区域间细粒度的关联性,该模块一方面在空间点通道中利用近邻算法找出每个搜索区域点的模板近邻点,并根据语义查询表提取对应特征;另一方面,在体素通道中以每个搜索区域点为中心构建局部多尺度体素,并根据落入体素单元的模板点索引计算语义查询表中值的累计和.最后,将双通道的特征融合并送入基于鸟瞰图的候选包围盒生成模块来回归目标包围盒.为了验证所提出方法的优越性,本文在KITTI和NuScenes数据集进行了测试,对比其他使用稀疏卷积的算法,本文方法平均成功率和精确率分别提升了11.0%和12.0%.本文方法在继承了稀疏卷积高效特点的同时还实现了跟踪精度的提高.

Disparity estimation for multi-scale multi-sensor fusion

The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.

基于多损失融合和混洗注意力的车载LiDAR点云道路标线提取方法

道路标线的准确提取在高级辅助驾驶系统和高精度地图的开发中具有重要意义。针对现有的基于阈值的车载激光点云道路标线提取方法在反射强度与点密度分布不均、道路标线与路面对比度低时提取效果较差的问题,本文提出了基于多损失融合和混洗注意力的车载LiDAR点云道路标线提取方法。选取典型高速公路试验样区进行道路标线提取试验,并与常规方法进行了精度对比分析。试验表明,本文方法在道路标线提取精度方面优于其他方法,有望更好地服务于自动驾驶的高精度地图开发应用。

无人机LiDAR及倾斜摄影测量技术在复杂河道工程测绘中的融合研究

复杂河道工程测绘中往往受到植被茂盛、信号链接弱、山体遮挡等多种因素影响,导致传统测绘手段难以开展,通过在复杂河道工程测绘中对无人机LiDAR及倾斜摄影测量技术进行对比研究,找出两者之间的互补优势及融合方法。经试验证明,融合纹理影像信息的LiDAR数据既具有准确的空间地理信息,又具有清晰的地物边界信息。采用LiDAR数据校正的倾斜模型细节表达明显改善,模型轮廓表达清晰,人工判读更加容易。该成果有效克服了无人机单一测量技术及传统测量技术的弊端,可快速构建更加精准的河道数字孪生模型。

果园机器人LiDAR/IMU紧耦合实时定位与建图方法

针对果园环境中GNSS定位信号易丢失和传统SLAM算法鲁棒性较差的问题,本文提出一种基于LiDAR/IMU紧耦合框架的全局无偏状态估计果园机器人定位与建图方法。LiDAR/IMU紧耦合框架基于因子图进行多源约束的IMU里程计构建,实时输出高频位姿信息,IMU里程计因子和预积分因子优化LiDAR里程计并提供位姿先验约束IMU零偏。引入局部点云地图参与特征点云粗匹配和非特征点云递进式匹配进一步稠密化源点云,改善LiDAR里程计的性能。融合GPS信号与LiDAR/IMU紧耦合框架的地图构建,能够得到准确且高频连续的位姿信息,提高点云地图的复用率。在果园和苗木等场景验证了该算法的性能,实验结果表明,与LIO-SAM等算法相比,定位精度维持在0.05 m左右,均方根误差为0.0162 m。本文算法使机器人具有更高的精度、实时性和鲁棒性,有效降低了系统累积误差,保证了所构建地图的全局一致性。

基于高光谱影像和LiDAR点云的城市地物分类实验方案设计

为更好地进行数据融合实现地物分类,设计了一种基于卷积神经网络(CNN)的高光谱与激光雷达数据融合分类实验方案。首先通过构建CNN模型实现高光谱及激光雷达数据的特征提取;然后分别在特征级和决策级上实现两种数据的融合,其中特征级融合采取求和与最大化策略,决策级融合采取加权融合策略;最后在休斯顿与特伦托数据集上使用标准训练集与测试集验证该模型的有效性。结果表明,相较于SVM和ELM两种对比方案,此方案总体分类精度分别提升了10.18%和8.75%。证实了新方案能够实现更高精度的城市地物分类。

A Fractal Dimension Based Framework for Night Vision Fusion

In this paper, a novel fusion framework is proposed for night-vision applications such as pedestrian recognition,vehicle navigation and surveillance. The underlying concept is to combine low-light visible and infrared imagery into a single output to enhance visual perception. The proposed framework is computationally simple since it is only realized in the spatial domain. The core idea is to obtain an initial fused image by averaging all the source images. The initial fused image is then enhanced by selecting the most salient features guided from the root mean square error(RMSE) and fractal dimension of the visual and infrared images to obtain the final fused image.Extensive experiments on different scene imaginary demonstrate that it is consistently superior to the conventional image fusion methods in terms of visual and quantitative evaluations.

Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-eastern Australia

We investigated a strategy to improve predicting capacity of plot-scale above-ground biomass （AGB） by fusion of LiDAR and Land- sat5 TM derived biophysical variables for subtropical rainforest and eucalypts dominated forest in topographically complex landscapes in North-eastern Australia. Investigation was carried out in two study areas separately and in combination. From each plot of both study areas, LiDAR derived structural parameters of vegetation and reflectance of all Landsat bands, vegetation indices were employed. The regression analysis was carded out separately for LiDAR and Landsat derived variables indi- vidually and in combination. Strong relationships were found with LiDAR alone for eucalypts dominated forest and combined sites compared to the accuracy of AGB estimates by Landsat data. Fusing LiDAR with Landsat5 TM derived variables increased overall performance for the eucalypt forest and combined sites data by describing extra variation （3% for eucalypt forest and 2% combined sites） of field estimated plot-scale above-ground biomass. In contrast, separate LiDAR and imagery data, andfusion of LiDAR and Landsat data performed poorly across structurally complex closed canopy subtropical minforest. These findings reinforced that obtaining accurate estimates of above ground biomass using remotely sensed data is a function of the complexity of horizontal and vertical structural diversity of vegetation.

Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels.This research develops a general framework to integrate ground-based and UAV-LiDAR(ULS)data to better estimate tree parameters based on quantitative structure modelling(QSM).This is accomplished in three sequential steps.First,the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy.Next,redundancy and noise were removed for the ground-based/ULS LiDAR data fusion.Finally,tree modeling and biophysical parameter retrieval were based on QSM.Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest,including poplar and dawn redwood species.Generally,ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data.The fusion-derived tree height,tree volume,and crown volume significantly improved by up to 9.01%,5.28%,and 18.61%,respectively,in terms of rRMSE.By contrast,the diameter at breast height(DBH)is the parameter that has the least benefits from fusion,and rRMSE remains approximately the same,because stems are already well sampled from ground data.Additionally,particularly for dense forests,the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR.Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests,whereby the improvement owing to fusion is not significant.

基于车载LiDAR的特征融合差分的车前道路提取方法

为应对行驶中变化的道路环境,划分当前车前道路的可行驶区域,本文提出一种基于多特征融合差分的车前道路检测方法。该算法通过形态学滤波法对原始点云进行地面点云提取,统计归纳地面点云数据,进而界定运算域,在运算域内划分不同纵深的差分元尺寸和起点,在差分元内进行特征参数的融合,形成特征矩阵,求解差分矩阵后再进行阈值滤波,进而实现车前道路点云的提取。本文首先与相关道路点云的提取算法进行对比,表明其性能优良;然后针对所采数据不同纵深的道路提取效果进行对比,证明该算法的有效性。

Vision Enhancement Technology of Drivers Based on Image Fusion

The rise of urban traffic flow highlights the growing importance of traffic safety.In order to reduce the occurrence rate of traffic accidents,and improve front vision information of vehicle drivers,the method to improve visual information of the vehicle driver in low visibility conditions is put forward based on infrared and visible image fusion technique.The wavelet image confusion algorithm is adopted to decompose the image into low-frequency approximation components and high-frequency detail components.Low-frequency component contains information representing gray value differences.High-frequency component contains the detail information of the image,which is frequently represented by gray standard deviation to assess image quality.To extract feature information of low-frequency component and high-frequency component with different emphases,different fusion operators are used separately by low-frequency and high-frequency components.In the processing of low-frequency component,the fusion rule of weighted regional energy proportion is adopted to improve the brightness of the image,and the fusion rule of weighted regional proportion of standard deviation is used in all the three high-frequency components to enhance the image contrast.The experiments on image fusion of infrared and visible light demonstrate that this image fusion method can effectively improve the image brightness and contrast,and it is suitable for vision enhancement of the low-visibility images.

Surrounding Objects Detection and Tracking for Autonomous Driving Using LiDAR and Radar Fusion

Radar and LiDAR are two environmental sensors commonly used in autonomous vehicles,Lidars are accurate in determining objects’positions but significantly less accurate as Radars on measuring their velocities.However,Radars relative to Lidars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution.In order to compensate for the low detection accuracy,incomplete target attributes and poor environmental adaptability of single sensors such as Radar and LiDAR,in this paper,an effective method for high-precision detection and tracking of surrounding targets of autonomous vehicles.By employing the Unscented Kalman Filter,Radar and LiDAR information is effectively fused to achieve high-precision detection of the position and speed information of targets around the autonomous vehicle.Finally,the real vehicle test under various driving environment scenarios is carried out.The experimental results show that the proposed sensor fusion method can effectively detect and track the vehicle peripheral targets with high accuracy.Compared with a single sensor,it has obvious advantages and can improve the intelligence level of autonomous cars.

LINS-GNSS:滤波与优化耦合的GNSS/INS/LiDAR巡检机器人定位方法

为了能够更加灵活地执行变电站巡检任务,非固定线路的机器人巡检技术越来越受到关注.如何在复杂的变电站环境中实现高精度的定位是机器人在变电站执行巡检任务时需要解决的核心问题.单一传感器难以满足变电站可靠定位的要求,因此,本文设计了多传感器融合的LINS-GNSS定位方法.其前端基于迭代误差状态卡尔曼滤波框架将激光雷达和惯性导航进行紧耦合,在每次迭代中生成新的特征对应关系递归地校正估计状态.后端使用因子图优化的方法将卫星导航的定位结果与LINS后端输出的定位结果松耦合.优化过程中先将局部坐标系与全局坐标系对齐,再将卫星导航的位置约束作为先验边添加到后端的因子图中,最后将定位结果在全局坐标系下输出.为了评估LINS-GNSS系统在变电站环境中的性能,本文在实际变电站中进行了测试.实验结果表明,LINS-GNSS系统在变电站环境中可以达到优于0.5 m的定位精度,且比现有最佳算法LIO-SAM定位精度更高.

利用UWB校正LiDAR SLAM的室内组合定位方法

针对室内定位中激光雷达(LiDAR)即时定位与地图构建(SLAM)算法误差会随时间累积以及超宽带(UWB)定位的结果较为分散的问题,提出了一种利用UWB观测值来校正LiDAR SLAM的组合定位方法。首先,通过里程计预测模型得到先验的位置估计信息;之后,利用粒子滤波融合LiDAR的观测值进行修正得到局部定位结果;最后,通过高斯滤波利用UWB观测值进行校正得到全局定位结果。实验结果表明:组合定位系统有效消除了LiDAR SLAM的累积误差,并且轨迹相对于UWB定位更为平滑。