DeepSLAM: A Robust Monocular SLAM System With Unsupervised Deep Learning

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.

Automated summary

DeepSLAM is an unsupervised deep learning-based visual SLAM system that uses stereo imagery for training and monocular image sequences for testing. It consists of essential components such as Mapping-Net, Tracking-Net, and Loop-Net, which enable it to generate pose estimates, depth maps, and outlier rejection masks simultaneously. The system demonstrates good performance in pose estimation accuracy and robustness in challenging scenes, as evaluated on various datasets.