基于神经网络的VSLAM综述

传统的基于视觉的SLAM技术成果颇丰,但在具有挑战性的环境中难以取得想要的效果.深度学习推动了计算机视觉领域的快速发展,并在图像处理中展现出愈加突出的优势.将深度学习与基于视觉的SLAM结合是一个热门话题,诸多研究人员的努力使二者的广泛结合成为可能.本文从深度学习经典的神经网络入手,介绍了深度学习与传统基于视觉的SLAM算法的结合,概述了卷积神经网络(CNN)与循环神经网络(RNN)在深度估计、位姿估计、闭环检测等方面的成就,分析了神经网络在语义信息提取方面的优点,以期为未来自主移动机器人真正自主化提供帮助.最后,对未来VSLAM发展进行了展望.

A review of visual SLAM based on neural networks

Although traditional vision-based SLAM (VSLAM) technologies have achieved impressive results,they are less satisfactory in challenging environments.Deep learning promotes the rapid development of computer vision and shows prominent advantages in image processing.It''s a hot spot to combine deep learning with VSLAM,which is promising through the efforts of many researchers.Here,we introduce the combination of deep learning and traditional VSLAM algorithm,starting from the classical neural networks of deep learning.The achievements of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in depth estimation,pose estimation and closed-loop detection are summarized.The advantages of neural network in semantic information extraction are elaborated,and the future development of VSLAM is also prospected.