This paper studies the geometric boundary representations for Inverse Lax-Wendroff(ILW)method,aiming to develop a practical computer-aided engineering method without body-fitted meshes.We propose the signed distance f...This paper studies the geometric boundary representations for Inverse Lax-Wendroff(ILW)method,aiming to develop a practical computer-aided engineering method without body-fitted meshes.We propose the signed distance function(SDF)representation of the geometric boundary and design an extremely efficient algorithm for foot point calculation,which is particularly in line with the needs of ILW.Theoretical and numerical analyses demonstrate that the SDF representation of geometric boundary can satisfy ILW’s needs better than others.The effectiveness and robustness of our proposed method are verified by simulating initial boundary value computational physical problems of Euler equation for compressible fluids.展开更多
由于原始TSDF(Truncated Signed Distance Function,TSDF)模型仅考虑相邻时间上的关联,误差将不可避免的累积到下一时刻,无法构建全局一致的地图。为了实时精确的建立大场景稠密3D地图,对TSDF模型进行了改进。首先,构筑相机位姿模型和...由于原始TSDF(Truncated Signed Distance Function,TSDF)模型仅考虑相邻时间上的关联,误差将不可避免的累积到下一时刻,无法构建全局一致的地图。为了实时精确的建立大场景稠密3D地图,对TSDF模型进行了改进。首先,构筑相机位姿模型和加权融合3D点截断信息的TSDF模型,用于准确表示创建物体的表面。其次,提出一种改进的回环检测方法,并将其与随机蕨类彩色图像编码化相结合,进而优化TSDF模型,即混合优化位姿模型。最后,使用g2o图优化库解算约束函数,建立数据集间的优化边。实验结果表明:混合优化位姿模型能识别曾到达区域,特别在较大场景下使用可以得到更加准确的相机轨迹和地图。采用TUM数据集中的fr1/xyz、fr1/room、fr1/desk对所提算法进行检验,结果表明该方法能够使相机轨迹的均方根误差分别下降0.59cm,3.14cm,0.94cm。在室内环境和公开数据集上的实验结果证明了所提算法的有效性和准确性。展开更多
文摘This paper studies the geometric boundary representations for Inverse Lax-Wendroff(ILW)method,aiming to develop a practical computer-aided engineering method without body-fitted meshes.We propose the signed distance function(SDF)representation of the geometric boundary and design an extremely efficient algorithm for foot point calculation,which is particularly in line with the needs of ILW.Theoretical and numerical analyses demonstrate that the SDF representation of geometric boundary can satisfy ILW’s needs better than others.The effectiveness and robustness of our proposed method are verified by simulating initial boundary value computational physical problems of Euler equation for compressible fluids.
文摘由于原始TSDF(Truncated Signed Distance Function,TSDF)模型仅考虑相邻时间上的关联,误差将不可避免的累积到下一时刻,无法构建全局一致的地图。为了实时精确的建立大场景稠密3D地图,对TSDF模型进行了改进。首先,构筑相机位姿模型和加权融合3D点截断信息的TSDF模型,用于准确表示创建物体的表面。其次,提出一种改进的回环检测方法,并将其与随机蕨类彩色图像编码化相结合,进而优化TSDF模型,即混合优化位姿模型。最后,使用g2o图优化库解算约束函数,建立数据集间的优化边。实验结果表明:混合优化位姿模型能识别曾到达区域,特别在较大场景下使用可以得到更加准确的相机轨迹和地图。采用TUM数据集中的fr1/xyz、fr1/room、fr1/desk对所提算法进行检验,结果表明该方法能够使相机轨迹的均方根误差分别下降0.59cm,3.14cm,0.94cm。在室内环境和公开数据集上的实验结果证明了所提算法的有效性和准确性。
