RUSSO：应对视觉退化的声纳优化鲁棒水下SLAM
RUSSO: Robust Underwater SLAM with Sonar Optimization against Visual Degradation
AI总结：
提出RUSSO水下SLAM系统，融合立体相机、IMU和成像声纳，在视觉退化时降级为声纳-惯性估计并利用声纳辅助初始化，显著提升了复杂水下环境中的定位鲁棒性与精度。
摘要：
水下环境中的视觉退化带来了独特且重大的挑战，这使得水下SLAM有别于地面上流行的基于视觉的SLAM。在本文中，我们提出了RUSSO，一种鲁棒的水下SLAM系统，该系统融合了立体相机、惯性测量单元（IMU）和成像声纳，以在充满挑战的水下环境中实现鲁棒且准确的定位，进行6自由度（DoF）估计。在视觉退化期间，系统降级为声纳-惯性系统，估计3-DoF位姿。声纳位姿估计作为IMU传播的强先验，从而增强了IMU传播位姿估计的可靠性。此外，我们提出了一种SLAM初始化方法，利用成像声纳来弥补SLAM初始化阶段视觉特征的缺失。我们通过在模拟器、水池和海洋场景中的实验广泛验证了RUSSO。结果表明，与最先进的视觉-惯性SLAM系统相比，RUSSO实现了更好的鲁棒性和定位精度，特别是在视觉上具有挑战性的场景中。据我们所知，这是首次融合立体相机、IMU和成像声纳来实现应对视觉退化的鲁棒水下SLAM。
英文摘要：
Visual degradation in underwater environments poses unique and significant challenges, which distinguishes underwater SLAM from popular vision-based SLAM on the ground. In this paper, we propose RUSSO, a robust underwater SLAM system which fuses stereo camera, inertial measurement unit (IMU), and imaging sonar to achieve robust and accurate localization in challenging underwater environments for 6 degrees of freedom (DoF) estimation. During visual degradation, the system is reduced to a sonar-inertial system estimating 3-DoF poses. The sonar pose estimation serves as a strong prior for IMU propagation, thereby enhancing the reliability of pose estimation with IMU propagation. Additionally, we propose a SLAM initialization method that leverages the imaging sonar to counteract the lack of visual features during the initialization stage of SLAM. We extensively validate RUSSO through experiments in simulator, pool, and sea scenarios. The results demonstrate that RUSSO achieves better robustness and localization accuracy compared to the state-of-the-art visual-inertial SLAM systems, especially in visually challenging scenarios. To the best of our knowledge, this is the first time fusing stereo camera, IMU, and imaging sonar to realize robust underwater SLAM against visual degradation.
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