中国激光, 2020, 47 (3): 0304004, 网络出版: 2020-03-12   

基于激光散斑的半稠密深度图获取算法 下载: 1351次

A Semi-Dense Depth Map Acquisition Algorithm Based on Laser Speckle
作者单位
华南理工大学机械与汽车工程学院, 广东 广州 510640
摘要
获取基于激光散斑的深度图像时存在匹配精度低、计算量大,以及在面对不同测量环境时鲁棒性差等问题,为此,提出了一种基于激光散斑的半稠密深度图获取算法。为解决鲁棒性差的问题,采用局部自适应二值化对散斑图像进行预处理,保证了窗口描述子的光照不变性;在测量精度方面,通过聚类算法提取出每个散斑的中心像素坐标,提高了每个散斑的位置准确度;在匹配成功率方面,将窗口描述子进行卷积得到简化的描述子,在减少计算量的同时增大了匹配成功率。最后根据匹配准则得到散斑配对点,再根据三角测距原理得到了每个散斑的深度值。实验结果表明:所提算法的鲁棒性较强,精度较高,提高了匹配成功率。
Abstract
Depth map acquisition, which is based on laser speckle, presents some issues, such as low matching precision, large amount of calculation, and poor robustness in different measurement environments. In this paper, a semi-dense depth map acquisition algorithm based on laser speckle is proposed to address these issues. The problem of poor robustness can be solved using the locally adaptive binarization, which preprocesses the speckle map to ensure the illumination invariance of the window descriptor. In terms of measurement accuracy, the central pixel coordinates of each speckle are extracted using a clustering algorithm, which improves the positional accuracy of each speckle. Regarding the matching success rate issue, the window descriptor is convoluted to obtain a simplified descriptor, which is able to reduce the amount of calculations and increase the matching success rate. Finally, the speckle pairing points are obtained according to the matching criterion, and then the depth values of each speckle are obtained according to the triangulation principle. Experiments confirm that the proposed algorithm is highly robust and accurate and improves the matching success rate.

古家威, 谢小鹏, 曹一波, 刘好新. 基于激光散斑的半稠密深度图获取算法[J]. 中国激光, 2020, 47(3): 0304004. Gu Jiawei, Xie Xiaopeng, Cao Yibo, Liu Haoxin. A Semi-Dense Depth Map Acquisition Algorithm Based on Laser Speckle[J]. Chinese Journal of Lasers, 2020, 47(3): 0304004.

本文已被 6 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!