融合2D激光雷达的室内单目深度估计

在室内单目视觉导航任务中,场景的深度信息十分重要.但单目深度估计是一个不适定问题,精度较低.目前, 2D激光雷达在室内导航任务中应用广泛,且价格低廉.因此,本文提出一种融合2D激光雷达的室内单目深度估计算法来提高深度估计精度.本文在编解码结构上增加了2D激光雷达的特征提取,通过跳跃连接增加单目深度估计结果的细节信息,并提出一种运用通道注意力机制融合2D激光雷达特征和RGB图像特征的方法.本文在公开数据集NYUDv2上对算法进行验证,并针对本文算法的应用场景,制作了带有2D激光雷达数据的深度数据集.实验表明,本文提出的算法在公开数据集和自制数据集中均优于现有的单目深度估计.

Indoor Monocular Depth Estimation by Fusing 2D LiDAR

The depth information of a scenario is very important in indoor monocular vision navigation tasks. However, monocular depth estimation is an ill-posed problem with low accuracy. At present, 2D LiDAR is widely used in indoor navigation tasks, and the price is low. Therefore, we propose an indoor monocular depth estimation algorithm by fusing 2D LiDAR to improve the accuracy of depth estimation. Specifically, the feature extraction of 2D LiDAR is added to the encoder-decoder structure, and skip connections are used to acquire more detailed information of monocular depth estimation. Additionally, a method using channel attention mechanisms is presented to fuse 2D LiDAR features and RGB image features. The algorithm is verified on the public dataset NYUDv2, and a depth dataset with 2D LiDAR data for the application scenarios of the algorithm is established. Experiments indicate that the proposed algorithm outperforms the state-of-art monocular depth estimation on both public dataset and self-made dataset.