结合改进密集模块深度估计网络和多视几何的视觉里程计
DOI:
作者:
作者单位:

上海电力大学

作者简介:

通讯作者:

中图分类号:

TP242.6

基金项目:

上海市"科技创新行动计划"高新技术领域项目(编号:21511101800)


Visual odometry combined with depth estimation network of improved dense block and multi-view geometry
Author:
Affiliation:

Shanghai University of Electric Power

Fund Project:

Shanghai "Science and Technology Innovation Action Plan" high-tech field project (No.21511101800)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    提出一种以多视图几何原理为基础,有效结合卷积神经网络进行图像深度估计和匹配筛选,构造无监督单目视觉里程计方法。针对主流深度估计网络易丢失图像浅层特征的问题,构造一种基于改进密集模块的深度估计网络,有效地聚合浅层特征,提升图像深度估计精度。里程计利用深度估计网络精确预测单目图像深度,利用光流网络获得双向光流,通过前后光流一致性原则筛选得高质量匹配。利用多视图几何原理和优化方式求解获得初始位姿和计算深度,并通过特定的尺度对齐原则得到全局尺度一致的6自由度位姿。同时,为了提高网络对场景细节和弱纹理区域的学习能力,将基于特征图合成的特征度量损失结合到网络损失函数中。在KITTI Odometry数据集上进行实验验证,不同阈值下的深度估计取得了85.9%、95.8%、97.2%的准确率。在09和10序列上进行里程计评估,绝对轨迹误差在0.007m。实验结果展现了所提出方法的有效性和准确性,证明了其在深度估计和视觉里程计任务上的性能优于现有方法。

    Abstract:

    An unsupervised monocular visual odometery based on the principle of multi-view geometry and effective combination of convolutional neural network for image depth estimation and matching screening. Aiming at the problem that mainstream depth estimation networks tend to lose the shallow features of images, a depth estimation network based on improved dense blocks is constructed to effectively aggregate shallow features and improve the accuracy of image depth estimation. The odometery uses the depth estimation network to accurately predict the depth of the monocular image, uses the optical flow network to obtain two-way optical flow, and selects a high-quality match based on the principle of frontward and backward optical flow consistency. The initial pose and calculated depth are obtained by using multi-view geometric principles and optimization methods, and a 6-degree-of-freedom pose with the fixed global scale is obtained through a specific scale alignment principle. At the same time, in order to improve the network's ability to learn scene details and the information of weak texture regions, the feature measurement loss based on feature map synthesis is combined into the network loss function. On the KITTI dataset, the depth estimation under different thresholds has achieved accuracy rates of 85.9%, 95.8%, and 97.2%, and the absolute trajectory error of the odometry on the 09 and 10 sequences is 0.007m.Experimental results show the effectiveness and accuracy of proposedmethod, and prove that it is superior to the existing methods on the task of visual odometry.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-07-20
  • 最后修改日期:2022-01-18
  • 录用日期:2022-01-28
  • 在线发布日期:
  • 出版日期: