Transformer Guided Geometry Model for Flow-Based Unsupervised Visual Odometry

• URL: http://arxiv.org/abs/2101.02143v1
• Date: Tue, 8 Dec 2020 19:39:26 GMT
• Title: Transformer Guided Geometry Model for Flow-Based Unsupervised Visual Odometry
• Authors: Xiangyu Li and Yonghong Hou and Pichao Wang and Zhimin Gao and Mingliang Xu and Wanqing Li
• Abstract summary: We propose a method consisting of two camera pose estimators that deal with the information from pairwise images. For image sequences, a Transformer-like structure is adopted to build a geometry model over a local temporal window. A Flow-to-Flow Pose Estimator (F2FPE) is proposed to exploit the relationship between pairwise images.
• Score: 38.20137500372927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing unsupervised visual odometry (VO) methods either match pairwise images or integrate the temporal information using recurrent neural networks over a long sequence of images. They are either not accurate, time-consuming in training or error accumulative. In this paper, we propose a method consisting of two camera pose estimators that deal with the information from pairwise images and a short sequence of images respectively. For image sequences, a Transformer-like structure is adopted to build a geometry model over a local temporal window, referred to as Transformer-based Auxiliary Pose Estimator (TAPE). Meanwhile, a Flow-to-Flow Pose Estimator (F2FPE) is proposed to exploit the relationship between pairwise images. The two estimators are constrained through a simple yet effective consistency loss in training. Empirical evaluation has shown that the proposed method outperforms the state-of-the-art unsupervised learning-based methods by a large margin and performs comparably to supervised and traditional ones on the KITTI and Malaga dataset.

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