The assembly of hybrid nanomaterials has opened up a new direction for the construction of high-performance anodes for lithium-ion batteries （LIBs）. In this work, we present a straightforward, eco-friendly, one-step...The assembly of hybrid nanomaterials has opened up a new direction for the construction of high-performance anodes for lithium-ion batteries （LIBs）. In this work, we present a straightforward, eco-friendly, one-step hydrothermal protocol for the synthesis of a new type of Fe2OB-SnO2/graphene hybrid, in which zero-dimensional （0D） SnO2 nanoparticles with an average diameter of 8 nm and one-dimensional （1D） Fe203 nanorods with a length of -150 nm are homogeneously attached onto two-dimensional （2D） reduced graphene oxide nanosheets, generating a unique point-line-plane （0D-1D-2D） architecture. The achieved Fe203-SnO2/graphene exhibits a well-defined morphology, a uniform size, and good monodispersity. As anode materials for LIBs, the hybrids exhibit a remarkable reversible capacity of 1,530 mA·g^-1 at a current density of 100 ma·g^-1 after 200 cycles, as well as a high rate capability of 615 mAh·g^-1 at 2,000 mA·g^-1 Detailed characterizations reveal that the superior lithium-storage capacity and good cycle stability of the hybrids arise from their peculiar hybrid nanostructure and conductive graphene matrix, as well as the synergistic interaction among the components.展开更多
现有基于点特征的视觉SLAM(simultaneous localization and mapping)算法在弱纹理环境中表现不佳,为此提出了一种基于点线面特征融合的视觉里程计算法,能够在弱纹理环境中实现精准定位。首先基于曼哈顿世界假设下,使用线特征与面特征提...现有基于点特征的视觉SLAM(simultaneous localization and mapping)算法在弱纹理环境中表现不佳,为此提出了一种基于点线面特征融合的视觉里程计算法,能够在弱纹理环境中实现精准定位。首先基于曼哈顿世界假设下,使用线特征与面特征提取曼哈顿世界坐标系,并将线特征与面特征与坐标系联合;其次为了提升系统定位的准确性,使用了一种无漂移旋转的位姿估计算法,将位姿的旋转与平移分开求解;最后利用结构化的线特征与面特征对位姿与曼哈顿轴进行优化,综合考虑图像中的点线面特征,使得位姿估计的结果更加精确。实验表明,该算法在TUM与ICL-NUIM数据集中的表现优于目前的其他方法。展开更多
结构化场景中,存在着低纹理表面为特征的人造环境,基于点特征的SLAM(Simultaneous Localization and Mapping,同时定位与地图构建)算法难以得到足够的匹配点对,从而导致相机估计运动失败。除了点之外,结构化环境提供了大量的几何特征,...结构化场景中,存在着低纹理表面为特征的人造环境,基于点特征的SLAM(Simultaneous Localization and Mapping,同时定位与地图构建)算法难以得到足够的匹配点对,从而导致相机估计运动失败。除了点之外,结构化环境提供了大量的几何特征,例如线和平面。因此,提出一种基于点线面特征融合的SLAM算法。算法将基于深度学习的SuperPoint点特征与传统线面特征相结合,利用结构化场景的特性,将位姿解耦细化。首先,使用线面特征构建MW(Manhattan World,曼哈顿世界)坐标系,利用每一时刻相机与MW坐标系的相对旋转得到相机之间的旋转矩阵;然后,构建点线面特征的重投影误差函数,通过最小化联合误差函数得到平移矩阵;最后,根据结构化环境下平面间相互垂直和平行的特性添加约束函数,同时为弥补环境中出现不严格遵守MW假设的情况,使用关键帧构建的局部地图投影到当前帧进一步优化位姿。在TUM公开数据集上与主流方法对比表明,该算法有效提升了结构化低纹理环境下的SLAM定位精度。展开更多
基金Acknowledgements The authors gratefully thank the financial support from the National Natural Science Foundation of China （Nos. 11275121, 21471096, and 21371116）, and Program for Innovative Research Team in University （No. IRT13078）.
文摘The assembly of hybrid nanomaterials has opened up a new direction for the construction of high-performance anodes for lithium-ion batteries （LIBs）. In this work, we present a straightforward, eco-friendly, one-step hydrothermal protocol for the synthesis of a new type of Fe2OB-SnO2/graphene hybrid, in which zero-dimensional （0D） SnO2 nanoparticles with an average diameter of 8 nm and one-dimensional （1D） Fe203 nanorods with a length of -150 nm are homogeneously attached onto two-dimensional （2D） reduced graphene oxide nanosheets, generating a unique point-line-plane （0D-1D-2D） architecture. The achieved Fe203-SnO2/graphene exhibits a well-defined morphology, a uniform size, and good monodispersity. As anode materials for LIBs, the hybrids exhibit a remarkable reversible capacity of 1,530 mA·g^-1 at a current density of 100 ma·g^-1 after 200 cycles, as well as a high rate capability of 615 mAh·g^-1 at 2,000 mA·g^-1 Detailed characterizations reveal that the superior lithium-storage capacity and good cycle stability of the hybrids arise from their peculiar hybrid nanostructure and conductive graphene matrix, as well as the synergistic interaction among the components.
文摘现有基于点特征的视觉SLAM(simultaneous localization and mapping)算法在弱纹理环境中表现不佳,为此提出了一种基于点线面特征融合的视觉里程计算法,能够在弱纹理环境中实现精准定位。首先基于曼哈顿世界假设下,使用线特征与面特征提取曼哈顿世界坐标系,并将线特征与面特征与坐标系联合;其次为了提升系统定位的准确性,使用了一种无漂移旋转的位姿估计算法,将位姿的旋转与平移分开求解;最后利用结构化的线特征与面特征对位姿与曼哈顿轴进行优化,综合考虑图像中的点线面特征,使得位姿估计的结果更加精确。实验表明,该算法在TUM与ICL-NUIM数据集中的表现优于目前的其他方法。
文摘结构化场景中,存在着低纹理表面为特征的人造环境,基于点特征的SLAM(Simultaneous Localization and Mapping,同时定位与地图构建)算法难以得到足够的匹配点对,从而导致相机估计运动失败。除了点之外,结构化环境提供了大量的几何特征,例如线和平面。因此,提出一种基于点线面特征融合的SLAM算法。算法将基于深度学习的SuperPoint点特征与传统线面特征相结合,利用结构化场景的特性,将位姿解耦细化。首先,使用线面特征构建MW(Manhattan World,曼哈顿世界)坐标系,利用每一时刻相机与MW坐标系的相对旋转得到相机之间的旋转矩阵;然后,构建点线面特征的重投影误差函数,通过最小化联合误差函数得到平移矩阵;最后,根据结构化环境下平面间相互垂直和平行的特性添加约束函数,同时为弥补环境中出现不严格遵守MW假设的情况,使用关键帧构建的局部地图投影到当前帧进一步优化位姿。在TUM公开数据集上与主流方法对比表明,该算法有效提升了结构化低纹理环境下的SLAM定位精度。
