基于无人机SfM及LaDiCaoz的地震地表破裂定量参数提取——以炉霍扎交村一带地震典型地表破裂为例

以炉霍县虾拉沱镇扎交村一带典型地震地表破裂为例,基于无人机SfM方法获取研究区高精度高分辨率的地形地貌数据,采用LaDiCaoz软件对其位错定量参数进行提取,并详细介绍相关技术流程。结果表明:1)LaDiCaoz得到的炉霍扎交村一带典型山脊位错地貌的水平位错为6.3(+0.5/-0.7)m,垂直位错为0.4(+0.1/-0.1)m,其水平位错量是受至少3次强震影响而累积的结果;2)无人机SfM方法可高效、便捷、低成本地获取小范围区域内高分辨率、高精度的地形地貌数据,数据质量可满足活动断裂的精细结构研究和定量参数提取,在川西高海拔地区有广阔的应用前景;3)利用LaDiCaoz进行位错提取,可进一步减小人为因素产生的测量误差,测量结果的可靠性可检验,实现了对活动断裂位错量提取的半自动化。

基于Leap Motion手势识别的三维交互系统

随着虚拟交互技术的发展,人们迈入了“体验式经济时代”,消费者越来越关注个性体验,因此,基于Leap Motion手势识别设备,设计了一种三维虚拟室内交互系统。该系统以Unity3D作为开发工具,Leap Motion作为硬件平台,结合C#语言进行脚本的编译,利用3ds Max平台对室内进行场景搭建,通过Unity3D工具将组件整合,设计了七种手势,使用Leap Motion硬件设备对场景中物体进行各种不同的操作。经试验表明,该系统实现了用户与场景中物体的交互能力,可以应用在室内装修和设计等方面,增强人们的体验感与趣味性。

考虑图像采集的退役零件SfM三维重建精度研究

运动恢复结构(SfM)是一种单目多视图三维重建技术。针对SfM多视图三维重建的速度及质量问题,提出了一种考虑图像采集的退役零件SfM三维重建精度优化方法。首先,构建了图像采集试验平台,以导靴和凸轮轴瓦盖为试验对象,选取图像质量、图像数量、拍摄距离及视角数量4个对重建质量有影响的参数为影响因素,每种因素取3个水平,设计了正交试验;然后,根据试验设计方案,对两个试验对象分别进行了9组图像采集,并用Reality Capture软件对其进行了三维建模;最后,根据重建点云和模型的准确性、重建的完整性与重建效率3个指标,对重建结果进行了评价,并进行了正交试验,通过极差分析获得了最优参数组合。研究结果表明:图像数量与视角数量对三维重建质量影响显著,拍摄距离与图像质量对三维重建质量影响不显著。该研究结果可为提高图像三维重建速度及质量提供参考。

基于UAV-SfM方法的黄土高原砒砂岩区侵蚀监测算法比较

[目的]为比较地形变化监测算法在黄土高原砒砂岩区的适用性。[方法]以皇甫川流域特拉沟一支沟为研究对象,采用无人机摄影测量技术获取2022年7月至2023年3月影像,结合SfM技术生成三维点云数据,比较分析[digital elevation model of difference(DoD)、cloud to cloud(C2C)、cloud to mesh(C2M)、multiscale model to model cloud comparison(M3C2)]等4种算法的侵蚀产沙监测精度,并分析点云密度变化对各方法精度的影响。[结果](1)4种常用算法在空间上都能监测到大幅度地表变化。其中,以M3C2算法的结果最优,线性拟合结果最好(R^(2)=0.953,p<0.01),且综合误差最小(MAE=0.0161 m,MRE=3.37%,RMSE=0.0194 m),C2M算法其次,DoD算法再次,而C2C算法结果最差。(2)通过比较,DoD算法仅适用于平坦区域的快速检测,坡度陡峭的区域监测侵蚀沉积量存在高估的现象。(3)M3C2和C2C算法对点云密度变化敏感,而C2M和DoD受点云密度变化影响较小。[结论]研究结果可为黄土高原砒砂岩地区基于UAV-SfM的侵蚀产沙监测方法的选择提供参考。

无序无人机影像的并行化SfM三维重建方法

增量式运动恢复结构(ISfM)已成为无人机影像三维重建的关键技术。然而,大数据量、大重叠度和高分辨率的无序无人机影像导致的匹配对检索代价大、迭代优化误差累积和效率低的问题,使其难以满足大场景ISfM处理需求。本文提出联合全局描述子和图索引的无人机影像并行化SfM方法。针对影像特征数量大、影像检索编码本尺寸增加导致的匹配对检索效率低的问题,设计了联合全局描述子和图索引的高效影像检索方法,从而加速影像匹配。针对分块并行化SfM子场景合并存在同名点搜索效率低、内存消耗大、合并解算精度低的问题,设计了基于按需匹配图和双向重投影误差的子场景合并方法,实现无人机影像的并行化SfM重建。利用不同场景、不同采集方式获取的真实无人机影像进行试验,结果表明本文方法能够实现36~108倍加速比的匹配对检索,ISfM重建效率达到30倍加速,且相对定向和绝对定向精度与传统方法相当。

Pulses in ground motions identified through surface partial matching and their impact on seismic rocking consequence

In seismology and earthquake engineering,it is fundamental to identify and characterize the pulse-like features in pulse-type ground motions.To capture the pulses that dominate structural responses,this study establishes congruence and shift relationships between response spectrum surfaces.A similarity search between spectrum surfaces,supplemented with a similarity search in time series,has been applied to characterize the pulse-like features in pulse-type ground motions.The identified pulses are tested in predicting the rocking consequences of slender rectangular blocks under the original ground motions.Generally,the prediction is promising for the majority of the ground motions where the dominant pulse is correctly identified.

Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

UAV SfM技术在活动构造研究中的应用——以青藏高原西北部龙木错断裂为例

为探讨搭载与未搭载实时动态差分技术/动态后处理技术(RTK/PPK)模块的无人机平台通过运动恢复结构(SfM)方法处理获得的数字高程模型(DEM)数据质量差异,以及建立不同无人机平台野外数据采集和室内数据处理过程的快速流程,利用大疆经纬M 300 RTK无人机(搭载禅思L1激光雷达(LiDAR)和测绘相机)与大疆精灵4 Pro无人机(搭载可见光相机)分别对青藏高原西北部龙木错断裂上1处位错阶地进行了数据采集,获得了该处高分辨率、高精度DEM数据和数字正射影像图(DOM)数据。对比结果显示,M 300 RTK无人机平台L1负载系统获得的LiDAR和SfM地形数据精度接近,两者在约100 m的飞行高度获得的DEM数据在水平和垂直方向的均方差分别为0.135 m、0.111 m和0.201 m、0.180 m;无RTK模块的精灵4 Pro无人机获取的DEM数据虽然绝对精度较差(水平和垂直方向均方差分别为1.707 m和249.280 m),但其反映的相对地形与实际地形接近,经过地面控制点校正后精度可以达到分米级。研究表明,RTK SfM技术克服了使用地面控制点的局限性,为活动构造研究领域微地貌测量提供了更高精度、更高效率的解决方案。当对测区的绝对三维坐标要求不高,仅需相对的地形起伏时,未搭载RTK模块的无人机也能够在无地面控制点约束的情况下满足地貌位错测量基本需求。

基于UAV-SfM数字模型的滇中环状构造地表与地形特征分析

地表特征与自然灾害密切相关,对维护生态环境和深入了解地表演化过程及地质构造特征具有重要作用。通过无人机(Unmanned aerial vehicle,UAV)航测和运动恢复结构(Structure from motion,SfM)技术构建的高空间分辨率数字模型,在滇中环状构造地貌开展地表覆盖信息和地形特征的分布关系分析。结果表明:在裸岩、裸土和植被混合区域,从定性和定量分析中发现DeepLabv3+算法相比于RF算法在试验区地表覆盖信息提取中有较好的提取效果。点云经滤波得到地面点,选择交叉验证中均值误差和均方根误差最小的Kriging算法构建分辨率0.1 m的数字高程模型(Digital elevation model,DEM),解译一阶坡面、二阶坡面和复合坡面的多种地形因子,根据相关性分析选取6种地形因子构建了综合地形分析模型(Comprehensive terrain analysis model,CTAM)。经过分析地表覆盖信息中覆盖面积最大裸土、植被与地形的联系,CTAM中每个等级像元数量与总像元数量百分比中,Ⅱ级占比最高,为28.87%,Ⅰ、Ⅳ、Ⅴ占比分别为18.39%、13.82%和17.29%。UAV-SfM技术能有效捕捉环状构造表面特征,可为该地区地质研究与资源管理提供技术手段和科学依据。

基于SFM的三维重建在点胶加工中的应用

为了实现点胶加工工件的三维模型重建,在分析了SFM(structure from motion)算法原理和五轴点胶机特点的基础上,提出了一种在五轴机床上使用单目相机实现工件三维模型重建的方法。首先,通过标定获得相机内外参,建立世界坐标系、机床坐标系与相机坐标系之间的关系;其次,从不同角度拍摄工件,与一般SFM不同的是拍摄时由机床机械坐标计算出相机位姿作为外参;最后,将图像序列和相机内外参作为输入,经过稀疏重建与稠密重建输出三维模型。实验结果表明,选用恰当的原始数据与参数可以高效地完成重建任务,重建成功率在90%以上,平均重投影误差为0.653 144 pixels。

基于Leap motion 的大学物理实验虚拟课堂设计

通过利用手势控制器(Leap Motion)技术,设计并实施一套创新的大学物理虚拟实验课程.通过在Unity3D软件中整合Leap Motion,创建一个大学物理实验测量杨氏模量实验场景,主要是利用C#语言进行开发,通过设计出多个模块,包括主界面设计和基本实验组件来完成物理实验的设计.在Leap Motion官网上下载关于Unity3D的SDK资源包并且导入Unity3D中,用其建立手部模型,实现Leap Motion控制器与Unity3D的手部交互实验系统的设计.人机交互技术的引入不仅可以丰富大学物理实验的教学手段,而且还能提高学生的学科理解和实际操作能力,同时培养其创新思维和科技素养.

Positron Emission Tomography Lung Image Respiratory Motion Correcting with Equivariant Transformer

In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://github.com/yehaowei/temt.

Safe Motion Planning and Control Framework for Automated Vehicles with Zonotopic TRMPC

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

基于Leap Motion手势交互技术的博物馆数字展示应用研究

随着科技的不断发展,博物馆数字展示已成为呈现文物与历史不可或缺的重要手段之一。在数字化潮流的推动下,Leap Motion技术凭借其卓越的手势交互能力引起了广泛的关注与兴趣。本研究旨在探索如何充分利用Leap Motion手势交互技术,设计并实现一种全新的博物馆数字展示交互系统,以提升观众的参与度和沉浸式体验。本研究采用实验研究方法,开发并实施了一套基于LeapMotion技术的手势交互系统。通过实验设计、设备选择与安装、手势识别与映射、展示内容的创建与集成、以及用户测试与优化等步骤完成研究。其结果表明,该系统能够准确捕捉观众的手部动作,并将其映射到超大屏幕的交互中,显著提升了展示效果和观众的互动体验。手势识别的误差率控制在5%以内,交互响应速度优秀,达到了预期目标。本研究验证了LeapMotion手势交互技术在博物馆数字展示中的有效性和可行性,提供了一种创新的展示方式,为公众呈现出更为丰富、引人入胜的文化体验,为未来博物馆展示的数字化发展方向提供有益的参考与借鉴,以促进整个行业的创新发展。

Multiparameter Numerical Investigation of Two Types of Moving Interactions Between the Deep-Sea Mining Vehicle Track Plate and Seabed Soil:Digging and Rotating Motions

To ensure the safe performance of deep-sea mining vehicles(DSMVs),it is necessary to study the mechanical characteristics of the interaction between the seabed soil and the track plate.The rotation and digging motions of the track plate are important links in the contact between the driving mechanism of the DSMV and seabed soil.In this study,a numerical simulation is conducted using the coupled Eulerian–Lagrangian(CEL)large deformation numerical method to investigate the interaction between the track plate of the DSMV and the seabed soil under two working conditions:rotating condition and digging condition.First,a soil numerical model is established based on the elastoplastic mechanical characterization using the basic physical and mechanical properties of the seabed soil obtained by in situ sampling.Subsequently,the soil disturbance mechanism and the dynamic mechanical response of the track plate under rotating and digging conditions are obtained through the analysis of the sensitivity of the motion parameters,the grouser structure,the layered soil features and the soil heterogeneity.The results indicate that the above parameters remarkably influence the interaction between the DSMV and the seabed soil.Therefore,it is important to consider the rotating and digging motion of the DSMV in practical engineering to develop a detailed optimization design of the track plate.

基于SFM数字化测绘的城墙遗产历史信息解译与病害研究——以罗马梅特尼亚城门为例

梅特尼亚城门是古罗马重要的通道,具有较高的历史价值,但目前存在较严重的病害。首先通过SFM方法对城门三维建模,提取砖石结构信息,识别主要病害,并解析成因。研究表明,遗产共有13种砖石结构,其中城门结构分布较多,城墙结构分布较少,与修整历史形成对应;共识别出11种病害,成因多与砖石结构及年代相关。研究建构了基于数字化技术与历史信息解读病害成因的城墙遗产研究框架,可为国内相关保护工作提供参考。

Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

Quantitative analysis of the morphing wing mechanism of raptors:IMMU-based motion capture system and its application on gestures of a Falco peregrinus

This paper presented a novel tinny motion capture system for measuring bird posture based on inertial and magnetic measurement units that are made up of micromachined gyroscopes, accelerometers, and magnetometers. Multiple quaternion-based extended Kalman filters were implemented to estimate the absolute orientations to achieve high accuracy.Under the guidance of ornithology experts, the extending/contracting motions and flapping cycles were recorded using the developed motion capture system, and the orientation of each bone was also analyzed. The captured flapping gesture of the Falco peregrinus is crucial to the motion database of raptors as well as the bionic design.

Bioinspired Polarized Optical Flow Enables Turbid Underwater Target Motion Estimation

Underwater target motion estimation is a challenge for ocean military and scientific research.In this work,we propose a method based on the combination of polarization imaging and optical flow for turbid underwater target detection.Polarization imaging can reduce the influence of backscattered light and obtain high-quality images underwater.The optical flow shows the motion and structural information of the target.We use polarized optical flow to obtain the optical flow field and estimate the target motion.The experimental results of different targets under varying water turbidity levels illustrate that our method is realizable and robust.The precision is verified by comparing the results with the precise displacement data and calculating two error measures.The proposed method based on polarized optical flow can obtain accurate displacement information and a good recognition effect.Moving target segmentation based on the Otsu method further proves the superiority of the polarized optical flow under turbid water.This study is valuable for target detection and motion estimation in scattering environments.