Semi-Direct Visual Odometry for Monocular , Wide-angle , and Multi-Camera Systems
Semi-Direct Visual Odometry for Monocular , Wide-angle , and Multi-Camera Systems
复制标题
适用于单目、广角和多摄像头系统的半直接视觉里程计
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
D. Scaramuzza
中科院分区：
文献类型：
--
作者：
Christian Forster;Zichao Zhang;M. Gassner;Manuel Werlberger;D. Scaramuzza
视觉里程计（VO）的直接方法已经得到普及，由于他们的能力，利用图像中的所有图像梯度的信息。然而，低计算速度以及缺少最优性和一致性的保证是直接方法的限制因素，其中建立的基于特征的方法反而成功。基于这些考虑，我们提出了一个半直接VO（SVO），它使用直接的方法来跟踪和三角测量像素，其特征在于高图像梯度，但依赖于成熟的基于特征的方法，用于结构和运动的联合优化。再加上一个强大的概率深度估计算法，这使我们能够有效地跟踪像素躺在弱角和边缘的环境中很少或高频纹理。我们进一步证明，该算法可以很容易地扩展到多个摄像机，跟踪边缘，包括运动先验，并使使用非常大的视场摄像机，如鱼眼和折反射的。基准数据集上的实验评估表明，该算法是显着的速度比最先进的，同时实现了极具竞争力的准确性。实验视频：http://rpg.ifi.uzh.ch/svo2
Direct methods for Visual Odometry (VO) have gained popularity due to their capability to exploit information from all image gradients in the image. However, low computational speed as well as missing guarantees for optimality and consistency are limiting factors of direct methods, where established feature-based methods instead succeed at. Based on these considerations, we propose a Semi-direct VO (SVO) that uses direct methods to track and triangulate pixels that are characterized by high image gradients but relies on proven feature-based methods for joint optimization of structure and motion. Together with a robust probabilistic depth estimation algorithm, this enables us to efficiently track pixels lying on weak corners and edges in environments with little or high-frequency texture. We further demonstrate that the algorithm can easily be extended to multiple cameras, to track edges, to include motion priors, and to enable the use of very large field of view cameras, such as fisheye and catadioptric ones. Experimental evaluation on benchmark datasets shows that the algorithm is significantly faster than the state of the art while achieving highly competitive accuracy. SUPPLEMENTARY MATERIAL Video of the experiments: http://rpg.ifi.uzh.ch/svo2