Direction navigability analysis is a supplement to the navigability analysis theory, in which extraction of the direction suitable-matching features(DSMFs) determines the evaluation performance. A method based on the ...Direction navigability analysis is a supplement to the navigability analysis theory, in which extraction of the direction suitable-matching features(DSMFs) determines the evaluation performance. A method based on the Gabor filter is proposed to estimate the direction navigability of the geomagnetic field. First,the DSMFs are extracted based on the Gabor filter’s responses.Second, in the view of pattern recognition, the classification accuracy in fault diagnosis is introduced as the objective function of the hybrid particle swarm optimization(HPSO) algorithm to optimize the Gabor filter’s parameters. With its guidance, the DSMFs are extracted. Finally, a direction navigability analysis model is established with the support vector machine(SVM), and the performances of the models under different objective functions are discussed. Simulation results show the parameters of the Gabor filter have a significant influence on the DSMFs, which, in turn, affects the analysis results of direction navigability. Moreover, the risk of misclassification can be effectively reduced by using the analysis model with optimal Gabor filter parameters. The proposed method is not restricted in geomagnetic navigation, and it also can be used in other fields such as terrain matching and gravity navigation.展开更多
视觉即时定位与建图(visual simultaneous localization and mapping,VSLAM)技术利用视觉传感器分析图像信息,使机器人在未知环境中实现自主定位和实时三维地图构建,是机器人导航和自动驾驶等任务的关键。为了给研究人员提供有价值的参...视觉即时定位与建图(visual simultaneous localization and mapping,VSLAM)技术利用视觉传感器分析图像信息,使机器人在未知环境中实现自主定位和实时三维地图构建,是机器人导航和自动驾驶等任务的关键。为了给研究人员提供有价值的参考,梳理了VSLAM的研究现状和最新进展。首先,深入探讨了机器人视觉SLAM算法,根据不同的传感器类型,概述了六种主流的视觉SLAM算法。对这些算法的基本原理进行系统分析,并对其中的经典算法进行了精炼总结。进一步地,将视觉SLAM算法分类为基于特征、基于直接法和基于学习的算法三大类,并详细探讨了各自的优缺点。最后,展望了视觉SLAM技术未来的发展方向,重点关注了深度学习、多传感器融合及实时性能优化等关键研究领域。展开更多
为降低高光谱影像的数据维数,提高地物分类识别效率,提出了一种地物分类方法——核直接线性判别分析(Kernel Direct Linear Discriminant Analysis,KDLDA)子空间法;并推导出类先验概率的一般形式下KDLDA的解。KDLDA子空间法先采用KDLDA...为降低高光谱影像的数据维数,提高地物分类识别效率,提出了一种地物分类方法——核直接线性判别分析(Kernel Direct Linear Discriminant Analysis,KDLDA)子空间法;并推导出类先验概率的一般形式下KDLDA的解。KDLDA子空间法先采用KDLDA提取遥感影像的非线性可分特征,然后在KDLDA子空间采用最小距离分类器进行分类识别。机载可见光/红外成像光谱仪(Airborne Visible/Infrared Imaging Spectrometer,AVIRIS)的高光谱影像识别结果表明,相比原空间法、LDA子空间法、直接线性判别分析(Direct Linear Discriminant Analysis,DLDA)子空间法、核线性判别分析(Kernel Linear Discriminant Analysis,KLDA)子空间法,KDLDA子空间法可显著提高识别效率。展开更多
基金supported by the Key Project of Military Research on Weapons and Equipment(2014551)
文摘Direction navigability analysis is a supplement to the navigability analysis theory, in which extraction of the direction suitable-matching features(DSMFs) determines the evaluation performance. A method based on the Gabor filter is proposed to estimate the direction navigability of the geomagnetic field. First,the DSMFs are extracted based on the Gabor filter’s responses.Second, in the view of pattern recognition, the classification accuracy in fault diagnosis is introduced as the objective function of the hybrid particle swarm optimization(HPSO) algorithm to optimize the Gabor filter’s parameters. With its guidance, the DSMFs are extracted. Finally, a direction navigability analysis model is established with the support vector machine(SVM), and the performances of the models under different objective functions are discussed. Simulation results show the parameters of the Gabor filter have a significant influence on the DSMFs, which, in turn, affects the analysis results of direction navigability. Moreover, the risk of misclassification can be effectively reduced by using the analysis model with optimal Gabor filter parameters. The proposed method is not restricted in geomagnetic navigation, and it also can be used in other fields such as terrain matching and gravity navigation.
文摘视觉即时定位与建图(visual simultaneous localization and mapping,VSLAM)技术利用视觉传感器分析图像信息,使机器人在未知环境中实现自主定位和实时三维地图构建,是机器人导航和自动驾驶等任务的关键。为了给研究人员提供有价值的参考,梳理了VSLAM的研究现状和最新进展。首先,深入探讨了机器人视觉SLAM算法,根据不同的传感器类型,概述了六种主流的视觉SLAM算法。对这些算法的基本原理进行系统分析,并对其中的经典算法进行了精炼总结。进一步地,将视觉SLAM算法分类为基于特征、基于直接法和基于学习的算法三大类,并详细探讨了各自的优缺点。最后,展望了视觉SLAM技术未来的发展方向,重点关注了深度学习、多传感器融合及实时性能优化等关键研究领域。
文摘为降低高光谱影像的数据维数,提高地物分类识别效率,提出了一种地物分类方法——核直接线性判别分析(Kernel Direct Linear Discriminant Analysis,KDLDA)子空间法;并推导出类先验概率的一般形式下KDLDA的解。KDLDA子空间法先采用KDLDA提取遥感影像的非线性可分特征,然后在KDLDA子空间采用最小距离分类器进行分类识别。机载可见光/红外成像光谱仪(Airborne Visible/Infrared Imaging Spectrometer,AVIRIS)的高光谱影像识别结果表明,相比原空间法、LDA子空间法、直接线性判别分析(Direct Linear Discriminant Analysis,DLDA)子空间法、核线性判别分析(Kernel Linear Discriminant Analysis,KLDA)子空间法,KDLDA子空间法可显著提高识别效率。
