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
Simultaneous localization and mapping （SLAM） is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major d...Simultaneous localization and mapping （SLAM） is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks： one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM （ISP-FastSLAM） algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter （ISPKF）, which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts： in the first part, an iterated sigma point particle filter （ISPPF） to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm.展开更多
同时定位与建图(simultaneous localization and mapping,SLAM)是具身智能机器人实现环境交互与自主决策的关键技术,目前基于三维激光雷达的SLAM算法大都是基于静态环境的,而动态物体的存在会导致激光SLAM算法的定位和建图精度降低。基...同时定位与建图(simultaneous localization and mapping,SLAM)是具身智能机器人实现环境交互与自主决策的关键技术,目前基于三维激光雷达的SLAM算法大都是基于静态环境的,而动态物体的存在会导致激光SLAM算法的定位和建图精度降低。基于此问题,详细阐述了国内外学者对动态激光SLAM算法的相关研究。根据动态物体检测原理的不同,将去除动态物体的方法分为基于语义分割、基于光线追踪、基于可见性等,并分析了每种方法的主要思想以及相关应用算法;对不同动态程度的物体进行了分类,总结了激光SLAM框架中不同类别动态物体对应的处理策略,并介绍了在线实时处理、离线后处理、终身SLAM策略的难点以及主流算法;归纳了动态激光SLAM算法主要的精度评价指标以及数据集;对动态激光SLAM算法未来的发展趋势进行了展望。展开更多
三维高斯溅射作为辐射场建模的前沿技术为煤矿井下数字化进程开辟了新路径,已成为煤矿机器人自主导航系统与数字孪生体系构建的关键支撑。然而,现有煤矿机器人SLAM(Simultaneous Localization and Mapping)方法在面对动态光照、深度感...三维高斯溅射作为辐射场建模的前沿技术为煤矿井下数字化进程开辟了新路径,已成为煤矿机器人自主导航系统与数字孪生体系构建的关键支撑。然而,现有煤矿机器人SLAM(Simultaneous Localization and Mapping)方法在面对动态光照、深度感知缺失与复杂几何结构等因素时面临着挑战。鉴于此,提出一种多维度视觉感知增强的高精度三维高斯溅射(3D Gaussian Splatting,3DGS)SLAM方法。首先,提出基于HIS(Hue-Intensity-Saturation)空间的多阶段图像增强校正算法,融合MSRCR(Multi-Scale Retinex with Color Restoration)、SWF(Side Window Filtering)和归一化伽马校正技术,通过非线性光照补偿与局部对比度优化实现RGB图像细节增强。构建彩色图-深度图特征引导的深度补全网络以填充深度数据空洞并抑制噪声干扰,提升原始数据质量。然后,设计混合欧几里德距离与重叠度结合的双阶段关键帧选择机制,通过空间差异性约束与视角覆盖冗余度控制,在保障场景表征完整性的前提下筛选最小必要关键帧集,实现精确高效关键帧选取。最后,设计透明度与时空加权观测频次双重约束的高斯管理策略,剔除低透明度区域的无效高斯椭球并对长期稳定观测的高斯椭球进行置信度强化,结合回环检测与位姿图优化实现全局一致的高质量建图。为验证所提方法有效性,依托自主设计集成的移动机器人平台,在采掘巷道等典型煤矿场景中进行了大量对比试验,结果表明:该方法在煤矿井下复杂场景中实现厘米级轨迹跟踪精度,其平均跟踪误差较当前最优基准方法降低12.4%;三维建图峰值信噪比(Peak Signal-to-Noise Ratio,PSNR)达22.4 dB并保持较高实时性,显著优于主流算法,为构建数字孪生矿山提供了可靠技术支撑。展开更多
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。
基金supported by Open Foundation of State Key Laboratory of Robotics and System, China (Grant No. SKLRS-2009-ZD-04)National Natural Science Foundation of China (Grant No. 60909055, Grant No.61005070)Fundamental Research Funds for the Central Universities of China (Grant No. 2009JBZ001-2)
文摘Simultaneous localization and mapping （SLAM） is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks： one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM （ISP-FastSLAM） algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter （ISPKF）, which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts： in the first part, an iterated sigma point particle filter （ISPPF） to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm.
文摘同时定位与建图(simultaneous localization and mapping,SLAM)是具身智能机器人实现环境交互与自主决策的关键技术,目前基于三维激光雷达的SLAM算法大都是基于静态环境的,而动态物体的存在会导致激光SLAM算法的定位和建图精度降低。基于此问题,详细阐述了国内外学者对动态激光SLAM算法的相关研究。根据动态物体检测原理的不同,将去除动态物体的方法分为基于语义分割、基于光线追踪、基于可见性等,并分析了每种方法的主要思想以及相关应用算法;对不同动态程度的物体进行了分类,总结了激光SLAM框架中不同类别动态物体对应的处理策略,并介绍了在线实时处理、离线后处理、终身SLAM策略的难点以及主流算法;归纳了动态激光SLAM算法主要的精度评价指标以及数据集;对动态激光SLAM算法未来的发展趋势进行了展望。
文摘三维高斯溅射作为辐射场建模的前沿技术为煤矿井下数字化进程开辟了新路径,已成为煤矿机器人自主导航系统与数字孪生体系构建的关键支撑。然而,现有煤矿机器人SLAM(Simultaneous Localization and Mapping)方法在面对动态光照、深度感知缺失与复杂几何结构等因素时面临着挑战。鉴于此,提出一种多维度视觉感知增强的高精度三维高斯溅射(3D Gaussian Splatting,3DGS)SLAM方法。首先,提出基于HIS(Hue-Intensity-Saturation)空间的多阶段图像增强校正算法,融合MSRCR(Multi-Scale Retinex with Color Restoration)、SWF(Side Window Filtering)和归一化伽马校正技术,通过非线性光照补偿与局部对比度优化实现RGB图像细节增强。构建彩色图-深度图特征引导的深度补全网络以填充深度数据空洞并抑制噪声干扰,提升原始数据质量。然后,设计混合欧几里德距离与重叠度结合的双阶段关键帧选择机制,通过空间差异性约束与视角覆盖冗余度控制,在保障场景表征完整性的前提下筛选最小必要关键帧集,实现精确高效关键帧选取。最后,设计透明度与时空加权观测频次双重约束的高斯管理策略,剔除低透明度区域的无效高斯椭球并对长期稳定观测的高斯椭球进行置信度强化,结合回环检测与位姿图优化实现全局一致的高质量建图。为验证所提方法有效性,依托自主设计集成的移动机器人平台,在采掘巷道等典型煤矿场景中进行了大量对比试验,结果表明:该方法在煤矿井下复杂场景中实现厘米级轨迹跟踪精度,其平均跟踪误差较当前最优基准方法降低12.4%;三维建图峰值信噪比(Peak Signal-to-Noise Ratio,PSNR)达22.4 dB并保持较高实时性,显著优于主流算法,为构建数字孪生矿山提供了可靠技术支撑。
