DeepSLAM: A Robust Monocular SLAM System With Unsupervised Deep Learning

Ruihao Li, Sen Wang, Dongbing Gu

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Abstract

In this article, we propose DeepSLAM, a novel unsupervised deep learning based visual simultaneous localization and mapping (SLAM) system. The DeepSLAM training is fully unsupervised since it only requires stereo imagery instead of annotating ground-truth poses. Its testing takes a monocular image sequence as the input. Therefore, it is a monocular SLAM paradigm. DeepSLAM consists of several essential components, including Mapping-Net, Tracking-Net, Loop-Net, and a graph optimization unit. Specifically, the Mapping-Net is an encoder and decoder architecture for describing the 3-D structure of environment, whereas the Tracking-Net is a recurrent convolutional neural network architecture for capturing the camera motion. The Loop-Net is a pretrained binary classifier for detecting loop closures. DeepSLAM can simultaneously generate pose estimate, depth map, and outlier rejection mask. In this article, we evaluate its performance on various datasets, and find that DeepSLAM achieves good performance in terms of pose estimation accuracy, and is robust in some challenging scenes.
Original languageEnglish
Pages (from-to)3577-3587
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Volume68
Issue number4
Early online date25 Mar 2020
DOIs
Publication statusE-pub ahead of print - 25 Mar 2020

Keywords

  • Depth estimation
  • machine learning
  • recurrent convolutional neural network (RCNN)
  • simultaneous localization and mapping (SLAM)
  • unsupervised deep learning (DL)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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