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High-precision self-supervised underwater visual odometry via unified image enhancement and coupled cycle consistency
Experimental results indicate that the proposed self-supervised monocular visual odometry framework achieves higher depth estimation and pose estimation accuracy than existing state-of-the-art monocular VO approaches, and results suggest that the proposed framework is well suited to challenging underwater environments.