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

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0.007 m.实验结果验证了所提出方法的有效性和准确性,表明其在深度估计和视觉里程计任务上的性能优于现有方法.

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

An unsupervised monocular visual odometry based on the principle of multi-view geometry and effective combination of the convolutional neural network for image depth estimation and correspondences selection is proposed. 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 odometry uses the depth estimation network to accurately predict the depth of the monocular image, uses the optical flow network to obtain forward-backward optical flow, and select high-quality correspondences based on the principle of forward 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 metric loss based on feature map synthesis is combined into the network loss function. On the KITTI Odometry 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 evaluation on the 09 and 10 sequences is 0.007m. Experimental results show the effectiveness and accuracy of the proposed method, and prove that it is superior to the existing methods on the task of visual odometry.