SurfSLAM: 基于仿真到现实的水下立体重建用于实时SLAM
SurfSLAM: Sim-to-Real Underwater Stereo Reconstruction For Real-Time SLAM
AI总结：
SurfSLAM通过仿真到现实的训练方法，结合立体相机与IMU、气压计和DVL测量，实现水下实时SLAM，提升水下环境下的立体估计和3D重建能力。
摘要：
定位与建图是水下机器人的核心感知能力。立体相机提供了一种低成本的直接估计度量深度的方法，以支持这些任务。然而，尽管在陆地上的立体深度估计取得了进展，但在水下场景中通过图像对计算深度仍然具有挑战性。在水下环境中，图像会受到光线衰减、视觉伪影和动态光照条件的破坏。此外，现实世界中的水下场景经常缺乏用于立体深度估计和3D重建的丰富纹理。因此，训练于空气中的立体估计网络无法直接转移到水下领域。此外，缺乏用于监督训练神经网络的真实水下立体数据集。糟糕的水下深度估计在基于立体的同步定位与建图（SLAM）算法中进一步恶化，使其成为水下机器人感知的根本挑战。为了解决这些挑战，我们提出了一种新的框架，该框架能够利用模拟数据和自监督微调对水下立体视差估计网络进行仿真到现实的训练。我们利用所学的深度预测来开发SurfSLAM，一种新的用于实时水下SLAM的框架，将立体相机与IMU、气压计和多普勒速度计（DVL）测量相结合。最后，我们使用水下机器人收集了一个具有挑战性的现实世界数据集，用于沉船调查。我们的数据集包含超过24,000对立体图像，以及高质量的密集摄影测量模型和参考轨迹用于评估。通过广泛的实验，我们证明了所提出训练方法在真实数据上改进水下立体估计以及实现复杂沉船遗址的准确轨迹估计和3D重建的优势。
英文摘要：
Localization and mapping are core perceptual capabilities for underwater robots. Stereo cameras provide a low-cost means of directly estimating metric depth to support these tasks. However, despite recent advances in stereo depth estimation on land, computing depth from image pairs in underwater scenes remains challenging. In underwater environments, images are degraded by light attenuation, visual artifacts, and dynamic lighting conditions. Furthermore, real-world underwater scenes frequently lack rich texture useful for stereo depth estimation and 3D reconstruction. As a result, stereo estimation networks trained on in-air data cannot transfer directly to the underwater domain. In addition, there is a lack of real-world underwater stereo datasets for supervised training of neural networks. Poor underwater depth estimation is compounded in stereo-based Simultaneous Localization and Mapping (SLAM) algorithms, making it a fundamental challenge for underwater robot perception. To address these challenges, we propose a novel framework that enables sim-to-real training of underwater stereo disparity estimation networks using simulated data and self-supervised finetuning. We leverage our learned depth predictions to develop SurfSLAM, a novel framework for real-time underwater SLAM that fuses stereo cameras with IMU, barometric, and Doppler Velocity Log (DVL) measurements. Lastly, we collect a challenging real-world dataset of shipwreck surveys using an underwater robot. Our dataset features over 24,000 stereo pairs, along with high-quality, dense photogrammetry models and reference trajectories for evaluation. Through extensive experiments, we demonstrate the advantages of the proposed training approach on real-world data for improving stereo estimation in the underwater domain and for enabling accurate trajectory estimation and 3D reconstruction of complex shipwreck sites.
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