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基于单个SIFT特征的相对位姿估计方法
Relative pose estimation method based on single SIFT features
投稿时间:2023-02-03  修订日期:2023-05-08
DOI:
中文关键词:  位姿估计  随机采样一致算法  自动驾驶  平面运动
英文关键词:Pose estimation  RANSAC  Autonomous driving  Planar motion
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目), 湖南省自然科学基金
作者单位邮编
谭泽 国防科技大学 410003
关棒磊* 国防科技大学 410003
孙祥一 国防科技大学 410003
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中文摘要:
      针对自动驾驶场景中的常见情况,我们提出了基于单个SIFT特征的平面相对位姿估计方法,通过将相机运动限制为三自由度的平面运动,进一步减少为仅有旋转角和平移角的两自由度的运动。同时考虑到地平面的单应信息包含有两幅图像间的相机运动和平面法向量的信息,描述了两幅图像间地面点对应的关系,可以通过估计单应矩阵来恢复相机的运动。为了减少使用的点对数量,我们进一步引入SIFT特征点,一对SIFT特征点包括两幅图像中对应点的图像坐标以及其特征旋转和特征尺度,大大扩充了单个点对所包含的信息。最终在仅使用一个SIFT特征的情况下完成对相机相对位姿的估计,最后采用随机采样一致算法对结果进行优化得到最终结果。我们在仿真实验及真实实验中与其他对比方法进行了误差对比,证明了我们提出的方法是相对可靠有效且高效的。
英文摘要:
      For common situations in autonomous driving scenarios, we propose a planar relative pose estimation method based on a single SIFT feature, which further reduces the motion to two degrees of freedom with only rotation angle and translation angle by limiting the camera movement to a plane motion of three degrees of freedom. At the same time, considering that the homography information of the ground plane contains the information of the camera motion and the plane normal vector between the two images, the corresponding relationship between the ground points between the two images is described, and the camera motion can be restored by estimating the homography matrix. In order to reduce the number of point pairs used, we further introduce SIFT feature points, a pair of SIFT feature points includes the image coordinates of the corresponding points in the two images, as well as their feature rotation and feature scale, which greatly expands the information contained in a single point pair. Finally, the estimation of the relative pose of the camera is completed using only one SIFT feature, and finally the random sampling consensus algorithm is used to optimize the results to obtain the final result. We compare errors with other comparison methods in simulation experiments and real experiments, which proves that the proposed method is relatively reliable, effective and efficient.
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