The selection of the optimal baseline in the front-view monocular vision system
The selection of the optimal baseline in the front-view monocular vision system
复制标题
前视单目视觉系统最优基线的选择
DOI：
10.1117/12.2283470
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
J. Tian
中科院分区：
文献类型：
--
作者：
Bincheng Xiong;Jun Zhang;Daimeng Zhang;Xiaomao Liu;J. Tian
在前视单目视觉系统中，深度场的求解精度与帧间基线的长度和图像匹配结果的精度有关。一般来说，基线长度越长，求解深度场的精度越高。但同时，帧间图像之间的差异增大，增加了图像匹配的难度，降低了匹配精度，最终可能导致深度场求解失败。通常的做法之一是使用跟踪匹配方法来提高图像之间的匹配精度，但这种算法容易造成间隔较大的图像之间的匹配漂移，导致图像匹配的累积误差，最终求解深度场的精度仍然很低。本文提出了一种基于最优基线长度的深度场融合算法。首先，分析了深度场计算精度与帧间基线长度之间的定量关系，并通过大量实验找到了最优的基线长度；其次，引入稀疏SLAM的深度反滤波技术，在基线最优长度约束下求解深度场；通过大量的实验，结果表明我们的算法可以有效地消除图像变化带来的失配，并且在大型基线场景下仍然可以正确求解景深场。该算法在时间复杂度和空间复杂度上都优于传统的SFM算法。通过大量实验得到的最优基线对前视单目镜景深的计算具有指导作用。
In the front-view monocular vision system, the accuracy of solving the depth field is related to the length of the inter-frame baseline and the accuracy of image matching result. In general, a longer length of the baseline can lead to a higher precision of solving the depth field. However, at the same time, the difference between the inter-frame images increases, which increases the difficulty in image matching and the decreases matching accuracy and at last may leads to the failure of solving the depth field. One of the usual practices is to use the tracking and matching method to improve the matching accuracy between images, but this algorithm is easy to cause matching drift between images with large interval, resulting in cumulative error in image matching, and finally the accuracy of solving the depth field is still very low. In this paper, we propose a depth field fusion algorithm based on the optimal length of the baseline. Firstly, we analyze the quantitative relationship between the accuracy of the depth field calculation and the length of the baseline between frames, and find the optimal length of the baseline by doing lots of experiments; secondly, we introduce the inverse depth filtering technique for sparse SLAM, and solve the depth field under the constraint of the optimal length of the baseline. By doing a large number of experiments, the results show that our algorithm can effectively eliminate the mismatch caused by image changes, and can still solve the depth field correctly in the large baseline scene. Our algorithm is superior to the traditional SFM algorithm in time and space complexity. The optimal baseline obtained by a large number of experiments plays a guiding role in the calculation of the depth field in front-view monocular.