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 sceneaggregated 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),SSNet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。
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.
作者
王恒
吴波
王振明
于剑峰
WANG Heng;WU Bo;WANG Zhenming;YU Jianfeng(Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai 201210,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《计算机工程与应用》
CSCD
北大核心
2022年第24期265-275,共11页
Computer Engineering and Applications
基金
上海市技术标准项目(19DZ2200600)
中国科学院上海微系统与信息技术研究所无线传感网与通信重点实验室开放课题(20190903)。
