A Front-End for Dense Monocular SLAM using a Learned Outlier Mask Prior

• URL: http://arxiv.org/abs/2104.00562v1
• Date: Thu, 1 Apr 2021 15:43:28 GMT
• Title: A Front-End for Dense Monocular SLAM using a Learned Outlier Mask Prior
• Authors: Yihao Zhang and John J. Leonard
• Abstract summary: Recent achievements in depth prediction from a single RGB image have powered the new research area of combining convolutional neural networks (CNNs) with classical simultaneous localization and mapping (SLAM) algorithms. Most of the current CNN-SLAM approaches have only taken advantage of the depth prediction but not yet other products from a CNN. We devise a dense CNN-assisted SLAM front-end that is implementable with sparse and evaluate it on both indoor and outdoor datasets.
• Score: 11.468537169201083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent achievements in depth prediction from a single RGB image have powered the new research area of combining convolutional neural networks (CNNs) with classical simultaneous localization and mapping (SLAM) algorithms. The depth prediction from a CNN provides a reasonable initial point in the optimization process in the traditional SLAM algorithms, while the SLAM algorithms further improve the CNN prediction online. However, most of the current CNN-SLAM approaches have only taken advantage of the depth prediction but not yet other products from a CNN. In this work, we explore the use of the outlier mask, a by-product from unsupervised learning of depth from video, as a prior in a classical probability model for depth estimate fusion to step up the outlier-resistant tracking performance of a SLAM front-end. On the other hand, some of the previous CNN-SLAM work builds on feature-based sparse SLAM methods, wasting the per-pixel dense prediction from a CNN. In contrast to these sparse methods, we devise a dense CNN-assisted SLAM front-end that is implementable with TensorFlow and evaluate it on both indoor and outdoor datasets.

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