Transformer-based model for monocular visual odometry: a video understanding approach

• URL: http://arxiv.org/abs/2305.06121v2
• Date: Tue, 12 Sep 2023 19:07:51 GMT
• Title: Transformer-based model for monocular visual odometry: a video understanding approach
• Authors: Andr\'e O. Fran\c{c}ani and Marcos R. O. A. Maximo
• Abstract summary: We deal with the monocular visual odometry as a video understanding task to estimate the 6-F camera's pose. We contribute by presenting the TS-DoVO model based on on-temporal self-attention mechanisms to extract features from clips and estimate the motions in an end-to-end manner. Our approach achieved competitive state-of-the-art performance compared with geometry-based and deep learning-based methods on the KITTI visual odometry dataset.
• Score: 0.9790236766474201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating the camera's pose given images of a single camera is a traditional task in mobile robots and autonomous vehicles. This problem is called monocular visual odometry and it often relies on geometric approaches that require considerable engineering effort for a specific scenario. Deep learning methods have shown to be generalizable after proper training and a large amount of available data. Transformer-based architectures have dominated the state-of-the-art in natural language processing and computer vision tasks, such as image and video understanding. In this work, we deal with the monocular visual odometry as a video understanding task to estimate the 6-DoF camera's pose. We contribute by presenting the TSformer-VO model based on spatio-temporal self-attention mechanisms to extract features from clips and estimate the motions in an end-to-end manner. Our approach achieved competitive state-of-the-art performance compared with geometry-based and deep learning-based methods on the KITTI visual odometry dataset, outperforming the DeepVO implementation highly accepted in the visual odometry community.

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