激光与光电子学进展, 2020, 57 (6): 061007, 网络出版: 2020-03-06   

融合扩张卷积网络与SLAM的无监督单目深度估计 下载: 1175次

Unsupervised Monocular Depth Estimation by Fusing Dilated Convolutional Network and SLAM
作者单位
上海工程技术大学电子电气工程学院, 上海 201600
摘要
针对由一般卷积神经网络预测的粗糙特征生成的深度图质量低、监督学习处理任务限制数据量等问题,提出一种新颖的融合扩张卷积神经网络和同时定位与建图(SLAM)的无监督单目深度估计方法。该方法采用视图重构的思想估计深度,利用光学一致性误差约束网络训练,扩大感受野,考虑图片细节特征。同时采用SLAM算法优化相机姿态,并将其嵌入视图重构框架中,实现单目图片与其深度图的直接映射。利用该方法在公开的KITTI数据集上进行实验,结果表明,与经典的sfmlearner方法相比,误差度量指标绝对差、平方差、均方差和对数均方差分别降低了0.032、0.634、1.095和0.026;准确率度量指标δ1、δ2和δ3分别提升了3.8%、2.6%和0.9%。该模型的可用性与稳健性得到验证。
Abstract
The quality of a depth map generated by coarse features which are predicted by convolutional neural networks (CNNs) is low. Meanwhile, strong-supervised methods strictly limit the data volume due to lack of labeling. To address these problems, an unsupervised monocular depth estimation method by fusing dilated convolutional neural network and simultaneous localization and mapping (SLAM) is proposed. This method adopts the idea of view reconstruction to estimate depth. Photo-consistency error is utilized in the method to constrain training, expand the field of view, and concern the image details. Traditional SLAM algorithm functions to globally optimize the camera pose and incorporate it into the reconstruction framework. Finally the straight correspondence between the input monocular image and its depth map is exploited. The method is evaluated on the public KITTI dataset. The evaluation results show that, compared with the classical sfmlearner method, the error indicators, including absolute relative difference, squared relative difference, root mean squared error, and log root mean squared error, decrease by 0.032, 0.634, 1.095, and 0.026 respectively, and the accuracy indicators, δ1, δ2 and δ3, increase by 3.8%, 2.6%, and 0.9% respectively. The availability and robustness of the proposed method are verified.

戴仁月, 方志军, 高永彬. 融合扩张卷积网络与SLAM的无监督单目深度估计[J]. 激光与光电子学进展, 2020, 57(6): 061007. Renyue Dai, Zhijun Fang, Yongbin Gao. Unsupervised Monocular Depth Estimation by Fusing Dilated Convolutional Network and SLAM[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061007.

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