Deep Online Correction for Monocular Visual Odometry

Autor:	Zhang, Jiaxin, Sui, Wei, Wang, Xinggang, Meng, Wenming, Zhu, Hongmei, Zhang, Qian
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Computer Science - Robotics
Druh dokumentu:	Working Paper
DOI:	10.1109/ICRA48506.2021.9561642
Popis:	In this work, we propose a novel deep online correction (DOC) framework for monocular visual odometry. The whole pipeline has two stages: First, depth maps and initial poses are obtained from convolutional neural networks (CNNs) trained in self-supervised manners. Second, the poses predicted by CNNs are further improved by minimizing photometric errors via gradient updates of poses during inference phases. The benefits of our proposed method are twofold: 1) Different from online-learning methods, DOC does not need to calculate gradient propagation for parameters of CNNs. Thus, it saves more computation resources during inference phases. 2) Unlike hybrid methods that combine CNNs with traditional methods, DOC fully relies on deep learning (DL) frameworks. Though without complex back-end optimization modules, our method achieves outstanding performance with relative transform error (RTE) = 2.0% on KITTI Odometry benchmark for Seq. 09, which outperforms traditional monocular VO frameworks and is comparable to hybrid methods. Comment: Accepted at 2021 IEEE International Conference on Robotics and Automation (ICRA)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.10029 Zobrazit plný text záznamu View this record from Arxiv