Scale-aware direct monocular odometry

• URL: http://arxiv.org/abs/2109.10077v1
• Date: Tue, 21 Sep 2021 10:30:15 GMT
• Title: Scale-aware direct monocular odometry
• Authors: Carlos Campos and Juan D. Tard\'os
• Abstract summary: We present a framework for direct monocular odometry based on depth prediction from a deep neural network. Our proposal largely outperforms classic monocular SLAM, being 5 to 9 times more precise, with an accuracy which is closer to that of stereo systems.
• Score: 4.111899441919165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a framework for direct monocular odometry based on depth prediction from a deep neural network. In contrast with existing methods where depth information is only partially exploited, we formulate a novel depth prediction residual which allows us to incorporate multi-view depth information. In addition, we propose to use a truncated robust cost function which prevents considering inconsistent depth estimations. The photometric and depth-prediction measurements are integrated in a tightly-coupled optimization leading to a scale-aware monocular system which does not accumulate scale drift. We demonstrate the validity of our proposal evaluating it on the KITTI odometry dataset and comparing it with state-of-the-art monocular and stereo SLAM systems. Experiments show that our proposal largely outperforms classic monocular SLAM, being 5 to 9 times more precise, with an accuracy which is closer to that of stereo systems.

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