An extended Kalman filter approach of simultaneous localization and mapping(SLAM) was proposed based on local maps. A local frame of reference was established periodically at the position of the robot, and then the ob...An extended Kalman filter approach of simultaneous localization and mapping(SLAM) was proposed based on local maps. A local frame of reference was established periodically at the position of the robot, and then the observations of the robot and landmarks were fused into the global frame of reference. Because of the independence of the local map, the approach does not cumulate the estimate and calculation errors which are produced by SLAM using Kalman filter directly. At the same time, it reduces the computational complexity. This method is proven correct and feasible in simulation experiments.展开更多
At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method o...At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method of variance reduction fast simultaneous localization and mapping(FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation,particle depletion and particle loss in traditional FastSLAM,which lead to the reduction of AUV location estimation accuracy.The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling.By increasing the weight of small particles and decreasing the weight of large particles,the variance of particle weight can be reduced,the number of effective particles can be increased,and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles.The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation,maintains the diversity of particles,weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system.展开更多
Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were de...Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms.展开更多
一当场，自我本地化系统为在有深入的 3D 里程碑的 3D 环境起作用的活动机器人被开发。机器人通过合并从 odometry 和单向性的照相机收集的信息的一个地图评估者递归地估计它的姿势。我们为这二个传感器造非线性的模型并且坚持说机器人...一当场，自我本地化系统为在有深入的 3D 里程碑的 3D 环境起作用的活动机器人被开发。机器人通过合并从 odometry 和单向性的照相机收集的信息的一个地图评估者递归地估计它的姿势。我们为这二个传感器造非线性的模型并且坚持说机器人运动和不精密的传感器大小的无常操作应该全部被嵌入并且追踪我们的系统。我们在一个概率的几何学观点和使用 unscented 变换描述无常框架宣传无常，它经历给定的非线性的功能。就我们的机器人的处理力量而言，图象特征在相应投射特征的附近被提取。另外，数据协会被统计距离评估。最后，一系列系统的实验被进行证明我们的系统的可靠、精确的性能。展开更多
在智慧矿山建设的背景下,智能化设备的应用日益成为矿山智慧化改造的主要内容,用于巡检、危险区域勘测等任务的煤矿井下智能机器人运行依赖于数字地图构建和机器人自身定位,但大多数传统的定位方法在煤矿井下出现了低效甚至失效的情况,...在智慧矿山建设的背景下,智能化设备的应用日益成为矿山智慧化改造的主要内容,用于巡检、危险区域勘测等任务的煤矿井下智能机器人运行依赖于数字地图构建和机器人自身定位,但大多数传统的定位方法在煤矿井下出现了低效甚至失效的情况,同步定位与建图技术(Simultaneous Localization and Mapping,SLAM)成为了煤矿井下智能机器人定位方法的较优选择。然而,受制于激光雷达的高成本,以及相机在井下的低光照环境性能不佳,需要设计一种兼顾低成本和具有井下低光照环境适应性的SLAM定位方法,故提出了一种具有井下暗光照适应性煤矿井下机器人定位方法。首先,采集了陕西省宝鸡市凤县某煤矿井下的实景图像和SLAM所需的相机与IMU数据,根据图像制作了非匹配的暗光与正常光数据集,经过数据扩增达到3560张图像。设计了结合自注意力模块的EnlightenGAN图像增强网络,在不依赖配对数据集的情况下兼顾图像不同区域的依赖关系应对图像光照不均区域。在ORB-SLAM3框架的基础上,引入全局部图像检测对输入图像进行筛分,引入基于解析解的IMU初始化改进策略提高初始化速度,并引入了改进的图像增强网络对低光照以及光照不均的图像进行增强处理。在EuRoC数据集上的试验表明,基于图像增强的煤矿井下智能机器人定位方法能够在低光照环境下降低13.7%的ERMS和15.24%的ESD。在2个实际煤矿巷道场景中,系统能够识别低光照环境、增加SLAM系统提取的特征点数量,减少定位轨迹的漂移现象,最终改善系统在巷道低光照区域的定位效果。展开更多
煤矿井下视觉同步定位与地图构建SLAM(Simultaneous Localization and Mapping)应用中,光照变化与低纹理场景严重影响特征点的提取和匹配结果,导致位姿估计失败,影响定位精度。提出一种基于改进定向快速旋转二值描述符ORB(Oriented Fast...煤矿井下视觉同步定位与地图构建SLAM(Simultaneous Localization and Mapping)应用中,光照变化与低纹理场景严重影响特征点的提取和匹配结果,导致位姿估计失败,影响定位精度。提出一种基于改进定向快速旋转二值描述符ORB(Oriented Fast and Rotated Brief)-SLAM3算法的煤矿井下移动机器人双目视觉定位算法SL-SLAM。针对光照变化场景,在前端使用光照稳定性的Super-Point特征点提取网络替换原始ORB特征点提取算法,并提出一种特征点网格限定法,有效剔除无效特征点区域,增加位姿估计稳定性。针对低纹理场景,在前端引入稳定的线段检测器LSD(Line Segment Detector)线特征提取算法,并提出一种点线联合算法,按照特征点网格对线特征进行分组,根据特征点的匹配结果进行线特征匹配,降低线特征匹配复杂度,节约位姿估计时间。构建了点特征和线特征的重投影误差模型,在线特征残差模型中添加角度约束,通过点特征和线特征的位姿增量雅可比矩阵建立点线特征重投影误差统一成本函数。局部建图线程使用ORB-SLAM3经典的局部优化方法调整点、线特征和关键帧位姿,并在后端线程中进行回环修正、子图融合和全局捆绑调整BA(Bundle Adjustment)。在EuRoC数据集上的试验结果表明,SL-SLAM的绝对位姿误差APE(Absolute Pose Error)指标优于其他对比算法,并取得了与真值最接近的轨迹预测结果:均方根误差相较于ORB-SLAM3降低了17.3%。在煤矿井下模拟场景中的试验结果表明,SL-SLAM能适应光照变化和低纹理场景,可以满足煤矿井下移动机器人的定位精度和稳定性要求。展开更多
矿山深部采空区已成为威胁矿山人员和生产设备安全的重要危险源。针对现有深部采空区探测方法成本高、时效性差、测量盲区多等问题,设计了一种探入式三维激光雷达扫描系统进行采空区探测。该系统采用廉价的机械旋转式激光雷达降低成本;...矿山深部采空区已成为威胁矿山人员和生产设备安全的重要危险源。针对现有深部采空区探测方法成本高、时效性差、测量盲区多等问题,设计了一种探入式三维激光雷达扫描系统进行采空区探测。该系统采用廉价的机械旋转式激光雷达降低成本;通过自主设计的基于图优化Cartographer-SLAM(Cartographer Simultaneous Localization and Mapping)算法,能够快速处理激光雷达数据,实现在井下实时定位与建图,提高了时效性;搭配探入式三维激光雷达扫描系统支架,可在危险巷道、采空区等人员难以进入的区域进行测量,有效减少测量盲区。为解决现有建模算法构建的采空区模型不光滑、孔洞多等问题,提出了一种基于移动最小二乘法(Moving Least Squares,MLS)优化的泊松曲面重建算法,对采空区点云数据进行建模,通过MLS法对数据点周围进行高阶多项式插值,经过八叉树分割、向量场计算、泊松方程求解、等值面提取,构建采空区三维模型。在辽宁省某金矿开展试验,通过采集多处采空区、巷道及地下硐室数据,实现了井下空间精确建模。试验结果表明:所设计的系统和算法,可高效、精确地实现深部复杂采空区三维建模,在一定程度上解决了深部复杂采空区探测中空区难以进入、存在测量盲区、建模精度不高等问题,为采空区管理和安全生产提供重要技术支持。展开更多
随着自动驾驶技术的快速发展,4D毫米波雷达因其全天候适应性和抗干扰能力,成为同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)的关键传感器。然而在隧道等封闭环境中,多径效应引发的虚警点云严重影响了雷达SLAM系统...随着自动驾驶技术的快速发展,4D毫米波雷达因其全天候适应性和抗干扰能力,成为同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)的关键传感器。然而在隧道等封闭环境中,多径效应引发的虚警点云严重影响了雷达SLAM系统的定位精度与建图效果。针对这一问题,本文基于对隧道中的毫米波雷达点云数据特性和散射角特征的规律分析提出了一种全新的滑窗动态滤波算法。该方法结合了点云的空间统计特性与邻域密度检测方法剔除离群噪声点云,利用雷达点云粗配准获得先验估计位姿,结合雷达点云俯仰向和方位向的三维散射角特征,实现对真实目标点云数据的区分和聚类。随后使用随机采样一致性算法(Random Sample Consensus,RANSAC)拟合隧道墙壁平面并构建隧道墙面模型。通过引入动态滑窗更新策略,利用拟合的隧道墙面模型与先验估计位姿实时更新当前姿态节点到墙面边界距离阈值,使用距离阈值进一步消除隧道空间以外的虚警点云和噪声点云,并在因子图优化框架下完成全局位姿修正与局部地图更新。本研究在真实的隧道环境中采集多个不同场景的数据进行实验验证,实验结果证明本研究提出的方法在有效降低虚警点云干扰的同时,显著提高了定位精度和建图质量,且能在复杂环境中保持较高的稳定性。本研究为提高4D毫米波雷达SLAM在封闭环境中的鲁棒性提供了新的技术思路和实现路径。展开更多
基金Project(60234030) supported by the National Natural Science Foundation of China project(A1420060159) supported by the National Basic Research
文摘An extended Kalman filter approach of simultaneous localization and mapping(SLAM) was proposed based on local maps. A local frame of reference was established periodically at the position of the robot, and then the observations of the robot and landmarks were fused into the global frame of reference. Because of the independence of the local map, the approach does not cumulate the estimate and calculation errors which are produced by SLAM using Kalman filter directly. At the same time, it reduces the computational complexity. This method is proven correct and feasible in simulation experiments.
基金supported by the National Science Fund of China under Grants 61603034China Postdoctoral Science Foundation under Grant 2019M653870XB+1 种基金Beijing Municipal Natural Science Foundation (3182027)Fundamental Research Funds for the Central Universities,China,FRF-GF-17-B44,and XJS191315
文摘At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method of variance reduction fast simultaneous localization and mapping(FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation,particle depletion and particle loss in traditional FastSLAM,which lead to the reduction of AUV location estimation accuracy.The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling.By increasing the weight of small particles and decreasing the weight of large particles,the variance of particle weight can be reduced,the number of effective particles can be increased,and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles.The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation,maintains the diversity of particles,weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system.
基金Projects(60234030 60404021) supported by the National Natural Science Foundation of China
文摘Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms.
基金Supported by National Natural Science Foundation of China(60605023,60775048)Specialized Research Fund for the Doctoral Program of Higher Education(20060141006)
文摘一当场，自我本地化系统为在有深入的 3D 里程碑的 3D 环境起作用的活动机器人被开发。机器人通过合并从 odometry 和单向性的照相机收集的信息的一个地图评估者递归地估计它的姿势。我们为这二个传感器造非线性的模型并且坚持说机器人运动和不精密的传感器大小的无常操作应该全部被嵌入并且追踪我们的系统。我们在一个概率的几何学观点和使用 unscented 变换描述无常框架宣传无常，它经历给定的非线性的功能。就我们的机器人的处理力量而言，图象特征在相应投射特征的附近被提取。另外，数据协会被统计距离评估。最后，一系列系统的实验被进行证明我们的系统的可靠、精确的性能。
文摘在智慧矿山建设的背景下,智能化设备的应用日益成为矿山智慧化改造的主要内容,用于巡检、危险区域勘测等任务的煤矿井下智能机器人运行依赖于数字地图构建和机器人自身定位,但大多数传统的定位方法在煤矿井下出现了低效甚至失效的情况,同步定位与建图技术(Simultaneous Localization and Mapping,SLAM)成为了煤矿井下智能机器人定位方法的较优选择。然而,受制于激光雷达的高成本,以及相机在井下的低光照环境性能不佳,需要设计一种兼顾低成本和具有井下低光照环境适应性的SLAM定位方法,故提出了一种具有井下暗光照适应性煤矿井下机器人定位方法。首先,采集了陕西省宝鸡市凤县某煤矿井下的实景图像和SLAM所需的相机与IMU数据,根据图像制作了非匹配的暗光与正常光数据集,经过数据扩增达到3560张图像。设计了结合自注意力模块的EnlightenGAN图像增强网络,在不依赖配对数据集的情况下兼顾图像不同区域的依赖关系应对图像光照不均区域。在ORB-SLAM3框架的基础上,引入全局部图像检测对输入图像进行筛分,引入基于解析解的IMU初始化改进策略提高初始化速度,并引入了改进的图像增强网络对低光照以及光照不均的图像进行增强处理。在EuRoC数据集上的试验表明,基于图像增强的煤矿井下智能机器人定位方法能够在低光照环境下降低13.7%的ERMS和15.24%的ESD。在2个实际煤矿巷道场景中,系统能够识别低光照环境、增加SLAM系统提取的特征点数量,减少定位轨迹的漂移现象,最终改善系统在巷道低光照区域的定位效果。
文摘煤矿井下视觉同步定位与地图构建SLAM(Simultaneous Localization and Mapping)应用中,光照变化与低纹理场景严重影响特征点的提取和匹配结果,导致位姿估计失败,影响定位精度。提出一种基于改进定向快速旋转二值描述符ORB(Oriented Fast and Rotated Brief)-SLAM3算法的煤矿井下移动机器人双目视觉定位算法SL-SLAM。针对光照变化场景,在前端使用光照稳定性的Super-Point特征点提取网络替换原始ORB特征点提取算法,并提出一种特征点网格限定法,有效剔除无效特征点区域,增加位姿估计稳定性。针对低纹理场景,在前端引入稳定的线段检测器LSD(Line Segment Detector)线特征提取算法,并提出一种点线联合算法,按照特征点网格对线特征进行分组,根据特征点的匹配结果进行线特征匹配,降低线特征匹配复杂度,节约位姿估计时间。构建了点特征和线特征的重投影误差模型,在线特征残差模型中添加角度约束,通过点特征和线特征的位姿增量雅可比矩阵建立点线特征重投影误差统一成本函数。局部建图线程使用ORB-SLAM3经典的局部优化方法调整点、线特征和关键帧位姿,并在后端线程中进行回环修正、子图融合和全局捆绑调整BA(Bundle Adjustment)。在EuRoC数据集上的试验结果表明,SL-SLAM的绝对位姿误差APE(Absolute Pose Error)指标优于其他对比算法,并取得了与真值最接近的轨迹预测结果:均方根误差相较于ORB-SLAM3降低了17.3%。在煤矿井下模拟场景中的试验结果表明,SL-SLAM能适应光照变化和低纹理场景,可以满足煤矿井下移动机器人的定位精度和稳定性要求。
文摘矿山深部采空区已成为威胁矿山人员和生产设备安全的重要危险源。针对现有深部采空区探测方法成本高、时效性差、测量盲区多等问题,设计了一种探入式三维激光雷达扫描系统进行采空区探测。该系统采用廉价的机械旋转式激光雷达降低成本;通过自主设计的基于图优化Cartographer-SLAM(Cartographer Simultaneous Localization and Mapping)算法,能够快速处理激光雷达数据,实现在井下实时定位与建图,提高了时效性;搭配探入式三维激光雷达扫描系统支架,可在危险巷道、采空区等人员难以进入的区域进行测量,有效减少测量盲区。为解决现有建模算法构建的采空区模型不光滑、孔洞多等问题,提出了一种基于移动最小二乘法(Moving Least Squares,MLS)优化的泊松曲面重建算法,对采空区点云数据进行建模,通过MLS法对数据点周围进行高阶多项式插值,经过八叉树分割、向量场计算、泊松方程求解、等值面提取,构建采空区三维模型。在辽宁省某金矿开展试验,通过采集多处采空区、巷道及地下硐室数据,实现了井下空间精确建模。试验结果表明:所设计的系统和算法,可高效、精确地实现深部复杂采空区三维建模,在一定程度上解决了深部复杂采空区探测中空区难以进入、存在测量盲区、建模精度不高等问题,为采空区管理和安全生产提供重要技术支持。
