The quick response code based artificial labels are applied to provide semantic concepts and relations of surroundings that permit the understanding of complexity and limitations of semantic recognition and scene only...The quick response code based artificial labels are applied to provide semantic concepts and relations of surroundings that permit the understanding of complexity and limitations of semantic recognition and scene only with robot's vision.By imitating spatial cognizing mechanism of human,the robot constantly received the information of artificial labels at cognitive-guide points in a wide range of structured environment to achieve the perception of the environment and robot navigation.The immune network algorithm was used to form the environmental awareness mechanism with "distributed representation".The color recognition and SIFT feature matching algorithm were fused to achieve the memory and cognition of scenario tag.Then the cognition-guide-action based cognizing semantic map was built.Along with the continuously abundant map,the robot did no longer need to rely on the artificial label,and it could plan path and navigate freely.Experimental results show that the artificial label designed in this work can improve the cognitive ability of the robot,navigate the robot in the case of semi-unknown environment,and build the cognizing semantic map favorably.展开更多
针对传统视觉SLAM(simultaneous localization and mapping)在动态环境下定位精度较低、稳健性较差、结合深度学习后实时性较差及无法构建稠密地图的问题,本文提出了一种基于ORB-SLAM3的改进算法。首先,采用轻量化SegFormer语义分割网络...针对传统视觉SLAM(simultaneous localization and mapping)在动态环境下定位精度较低、稳健性较差、结合深度学习后实时性较差及无法构建稠密地图的问题,本文提出了一种基于ORB-SLAM3的改进算法。首先,采用轻量化SegFormer语义分割网络,对图像中存在的动态物体进行识别后,添加掩膜图像自适应膨胀方法,根据特征点数自动调整掩膜膨胀范围,更有效地保留静态特征点及去除潜在动态特征点;然后,改进词袋模型,提升算法的加载和匹配速度;最后,添加稠密建图线程,根据掩膜信息和关键帧,构建去除动态特征后的稠密点云地图。试验结果表明,该算法在动态场景下能够有效地剔除动态物体特征点,提高了系统的定位精度和稳健性,平均处理速度为20帧/s,基本满足实时运行的要求。展开更多
针对动态环境中实时定位与建图(Simultaneous Localization and Mapping,SLAM)算法位姿估计存在的定位漂移、实时性差等问题,提出一个名为YSG-SLAM的实时语义RGB-D SLAM系统。为了提高系统实时性,新增两个并行线程:一个用于获取二维语...针对动态环境中实时定位与建图(Simultaneous Localization and Mapping,SLAM)算法位姿估计存在的定位漂移、实时性差等问题,提出一个名为YSG-SLAM的实时语义RGB-D SLAM系统。为了提高系统实时性,新增两个并行线程:一个用于获取二维语义信息的语义分割线程,一个语义建图线程。为优化系统在处理动态物体时的准确性和鲁棒性,YSG-SLAM引入快速动态特征剔除算法,并耦合漏检补偿模块来应对基于实时实例分割(You Only Look At Coefficients,YOLACT)算法可能出现的漏检情况,有效地提升了特征点剔除的精确度和系统的整体稳定性。为减少由特征点聚集引起的定位误差从而优化特征点的空间分布,设计自适应角点提取阈值计算方法,使特征分布更加均匀。语义建图线程充分利用二维语义信息与三维点云数据,可选择性构建语义地图和八叉树地图,提高了系统的环境感知能力及机器人在复杂环境下的相关任务执行能力。YSG-SLAM在德国慕尼黑工业大学数据集、Bonn数据集上进行了评估,相较于原ORB-SLAM2,各项定位误差下降达93%。实验结果表明,YSG-SLAM有效提升了系统实时性,定位精度高,且可构建两种地图,具有一定的实用价值。展开更多
基金Projects(61203330,61104009,61075092)supported by the National Natural Science Foundation of ChinaProject(2013M540546)supported by China Postdoctoral Science Foundation+2 种基金Projects(ZR2012FM031,ZR2011FM011,ZR2010FM007)supported by Shandong Provincal Nature Science Foundation,ChinaProjects(2011JC017,2012TS078)supported by Independent Innovation Foundation of Shandong University,ChinaProject(201203058)supported by Shandong Provincal Postdoctoral Innovation Foundation,China
文摘The quick response code based artificial labels are applied to provide semantic concepts and relations of surroundings that permit the understanding of complexity and limitations of semantic recognition and scene only with robot's vision.By imitating spatial cognizing mechanism of human,the robot constantly received the information of artificial labels at cognitive-guide points in a wide range of structured environment to achieve the perception of the environment and robot navigation.The immune network algorithm was used to form the environmental awareness mechanism with "distributed representation".The color recognition and SIFT feature matching algorithm were fused to achieve the memory and cognition of scenario tag.Then the cognition-guide-action based cognizing semantic map was built.Along with the continuously abundant map,the robot did no longer need to rely on the artificial label,and it could plan path and navigate freely.Experimental results show that the artificial label designed in this work can improve the cognitive ability of the robot,navigate the robot in the case of semi-unknown environment,and build the cognizing semantic map favorably.
文摘针对传统视觉SLAM(simultaneous localization and mapping)在动态环境下定位精度较低、稳健性较差、结合深度学习后实时性较差及无法构建稠密地图的问题,本文提出了一种基于ORB-SLAM3的改进算法。首先,采用轻量化SegFormer语义分割网络,对图像中存在的动态物体进行识别后,添加掩膜图像自适应膨胀方法,根据特征点数自动调整掩膜膨胀范围,更有效地保留静态特征点及去除潜在动态特征点;然后,改进词袋模型,提升算法的加载和匹配速度;最后,添加稠密建图线程,根据掩膜信息和关键帧,构建去除动态特征后的稠密点云地图。试验结果表明,该算法在动态场景下能够有效地剔除动态物体特征点,提高了系统的定位精度和稳健性,平均处理速度为20帧/s,基本满足实时运行的要求。
文摘针对动态环境中实时定位与建图(Simultaneous Localization and Mapping,SLAM)算法位姿估计存在的定位漂移、实时性差等问题,提出一个名为YSG-SLAM的实时语义RGB-D SLAM系统。为了提高系统实时性,新增两个并行线程:一个用于获取二维语义信息的语义分割线程,一个语义建图线程。为优化系统在处理动态物体时的准确性和鲁棒性,YSG-SLAM引入快速动态特征剔除算法,并耦合漏检补偿模块来应对基于实时实例分割(You Only Look At Coefficients,YOLACT)算法可能出现的漏检情况,有效地提升了特征点剔除的精确度和系统的整体稳定性。为减少由特征点聚集引起的定位误差从而优化特征点的空间分布,设计自适应角点提取阈值计算方法,使特征分布更加均匀。语义建图线程充分利用二维语义信息与三维点云数据,可选择性构建语义地图和八叉树地图,提高了系统的环境感知能力及机器人在复杂环境下的相关任务执行能力。YSG-SLAM在德国慕尼黑工业大学数据集、Bonn数据集上进行了评估,相较于原ORB-SLAM2,各项定位误差下降达93%。实验结果表明,YSG-SLAM有效提升了系统实时性,定位精度高,且可构建两种地图,具有一定的实用价值。
