VISO：具有光度渲染的鲁棒水下视觉-惯性-声纳SLAM用于密集3D重建
VISO: Robust Underwater Visual-Inertial-Sonar SLAM with Photometric Rendering for Dense 3D Reconstruction
AI总结：
VISO通过融合视觉、惯性与声纳数据，结合光度渲染技术，实现高精度水下6自由度定位和实时密集3D重建。
摘要：
水下环境中的视觉挑战显著阻碍了基于视觉的定位精度和高保真的密集重建。在本文中，我们提出VISO，一种鲁棒的水下SLAM系统，融合了立体相机、惯性测量单元（IMU）和3D声纳，以实现准确的6自由度定位，并通过高光度保真度实现高效的密集3D重建。我们引入了一种从粗到细的在线校准方法，用于估计3D声纳与相机之间的外参。此外，提出了一种光度渲染策略，用于3D声纳点云，以丰富声纳地图的视觉信息。在实验室水槽和开放湖泊中的广泛实验表明，VISO在定位鲁棒性和准确性方面超过了当前最先进的水下和基于视觉的SLAM算法，同时在实时密集3D重建性能方面与离线密集映射方法相当。
英文摘要：
Visual challenges in underwater environments significantly hinder the accuracy of vision-based localisation and the high-fidelity dense reconstruction. In this paper, we propose VISO, a robust underwater SLAM system that fuses a stereo camera, an inertial measurement unit (IMU), and a 3D sonar to achieve accurate 6-DoF localisation and enable efficient dense 3D reconstruction with high photometric fidelity. We introduce a coarse-to-fine online calibration approach for extrinsic parameters estimation between the 3D sonar and the camera. Additionally, a photometric rendering strategy is proposed for the 3D sonar point cloud to enrich the sonar map with visual information. Extensive experiments in a laboratory tank and an open lake demonstrate that VISO surpasses current state-of-the-art underwater and visual-based SLAM algorithms in terms of localisation robustness and accuracy, while also exhibiting real-time dense 3D reconstruction performance comparable to the offline dense mapping method.
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