Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (24): 265-275.DOI: 10.3778/j.issn.1002-8331.2106-0191

• Engineering and Applications • Previous Articles     Next Articles

SLAM System Based on Depth Estimation Network SS-Net

WANG Heng, WU Bo, WANG Zhenming, YU Jianfeng   

  1. 1.Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
    2.University of Chinese Academy of Sciences, Beijing 100049, China
  • Online:2022-12-15 Published:2022-12-15

基于深度估计网络SS-Net的SLAM系统

王恒,吴波,王振明,于剑峰   

  1. 1.中国科学院 上海高等研究院,上海 201210
    2.中国科学院大学,北京 100049

Abstract: The monocular SLAM system cannot satisfy high-precision positioning in the existing SLAM method. Therefore, a SLAM system based on depth estimation network is proposed. This system integrates the sobel-boundary-induced and scene-aggregated network(SS-Net) system on the traditional ORB-SLAM system, and rely on monocular to achieve accuracy localization. The SS-Net uses for depth estimation considers the important role of the different depth relationship and boundary in depth prediction. Based on boundary-induced and scene-aggregated network(BS-Net), SS-Net incorporates an edge detection(ED) block, and a stripe refinement(SR) block. The SS-Net network can consider the deep correlation between different regions, extract important edges, and integrate network features below different levels, it can process a single frame of image, and then obtain depth estimate of the entire sequence. Experiments on NYUD v2 and TUM datasets show that SS-Net depth prediction has higher accuracy, and proves that the performance of SLAM system based on SS-Net is better than the original system.

Key words: depth estimation, simultaneous localization and mapping(SLAM), deep learning, indoor positioning, supervised network

摘要: 现有的SLAM方案中,单目SLAM系统无法满足高精度定位。因此提出了一种基于深度估计网络的SLAM系统。此系统在ORB-SLAM的系统上,融合了Sobel边界引导和场景聚合网络(sobel-boundary-induced and scene-aggregated network,SS-Net)的系统,仅依靠单目实现精准定位。SS-Net考虑了不同区域的深度关系和边界在深度预测中的重要特征。基于边界引导和场景聚合网络(boundary-induced and scene-aggregated network,BS-Net),SS-Net提出了边界提取模块(edge detection,ED),改进了图像细化模块(stripe refinement,SR)。SS-Net网络能够考虑不同区域之间的深度相关性,提取重要的边缘,并融合不同层次下面的网络特征,可以处理单帧图像,从而获得整个序列的深度估计。在NYUD v2和TUM数据集上的大量实验表明,SS-Net深度预测有较高的准确性,并且证明了基于SS-Net的SLAM系统比原系统更优秀。

关键词: 深度估计, 同时定位与地图绘制(SLAM), 深度学习, 室内定位, 有监督网络