Abstract
In this paper, we proposed a new deep learning based dense monocular SLAM method. Compared to existing methods, the proposed framework constructs a dense 3D model via a sparse to dense mapping using learned surface normals. With single view learned depth estimation as prior for monocular visual odometry, we obtain both accurate positioning and high quality depth reconstruction. The depth and normal are predicted by a single network trained in a tightly coupled manner.Experimental results show that our method significantly improves the performance of visual tracking and depth prediction in comparison to the state-of-the-art in deep monocular dense SLAM.
Abstract (translated)
本文提出了一种新的基于深度学习的密集单眼冲击法。与现有的方法相比，该框架通过稀疏到密集的映射，利用学习的曲面法线构造了一个密集的三维模型。采用单视学深度估计作为单目视觉里程测量的先例，既获得了精确的定位，又获得了高质量的深度重建。通过一个紧耦合训练的单网络对深度和法向进行预测，实验结果表明，该方法较之现有的深单目重撞击深度跟踪和深度预测技术，能显著提高视觉跟踪和深度预测的性能。
URL
https://arxiv.org/abs/1903.09199
