Attenuation-Aware Weighted Optical Flow with Medium Transmission Map for Learning-based Visual Odometry in Underwater terrain

• URL: http://arxiv.org/abs/2407.13159v1
• Date: Thu, 18 Jul 2024 05:00:15 GMT
• Title: Attenuation-Aware Weighted Optical Flow with Medium Transmission Map for Learning-based Visual Odometry in Underwater terrain
• Authors: Bach Nguyen Gia, Chanh Minh Tran, Kamioka Eiji, Tan Phan Xuan,
• Abstract summary: This paper addresses the challenge of improving learning-based monocular visual odometry (VO) in underwater environments. The novel wflow-TartanVO is introduced, enhancing the accuracy of VO systems for autonomous underwater vehicles (AUVs) Evaluation of different real-world underwater datasets demonstrates the outperformance of wflow-TartanVO over baseline VO methods.
• Score: 0.03749861135832072
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses the challenge of improving learning-based monocular visual odometry (VO) in underwater environments by integrating principles of underwater optical imaging to manipulate optical flow estimation. Leveraging the inherent properties of underwater imaging, the novel wflow-TartanVO is introduced, enhancing the accuracy of VO systems for autonomous underwater vehicles (AUVs). The proposed method utilizes a normalized medium transmission map as a weight map to adjust the estimated optical flow for emphasizing regions with lower degradation and suppressing uncertain regions affected by underwater light scattering and absorption. wflow-TartanVO does not require fine-tuning of pre-trained VO models, thus promoting its adaptability to different environments and camera models. Evaluation of different real-world underwater datasets demonstrates the outperformance of wflow-TartanVO over baseline VO methods, as evidenced by the considerably reduced Absolute Trajectory Error (ATE). The implementation code is available at: https://github.com/bachzz/wflow-TartanVO

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